feat(metrics): add support for multiple dimension sets

[oyiz-michael]

Issue number: closes #6198

Summary

This PR implements support for multiple dimension sets in the Metrics utility, enabling users to create aggregated CloudWatch metrics across different dimension combinations. This aligns with the CloudWatch Embedded Metric Format (EMF) specification and brings Python implementation to feature parity with other Powertools runtimes (Java, TypeScript).

Changes

Core Implementation:

• Added add_dimensions(dimensions: dict[str, str]) method to AmazonCloudWatchEMFProvider
• Added dimension_sets: list[dict[str, str]] to track multiple dimension arrays
• Updated serialize_metric_set() to output all dimension sets in the EMF Dimensions array
• Modified clear_metrics() to properly clear dimension sets between invocations

API Surface:

• Added add_dimensions() method to the Metrics class for easy access
• Method accepts a dictionary of dimension name-value pairs
• Default dimensions are automatically included in all dimension sets added after they're configured

Behavior:

• When dimension keys overlap across sets, the last value wins in the EMF output root
• All dimensions are validated (non-empty names/values, max 30 unique dimensions)
• Empty dimension dictionaries trigger warnings but don't raise errors
• Dimension values are automatically converted to strings

Testing:

• Added 13 comprehensive functional tests covering:
  • Multiple dimension set creation and ordering
  • Default dimensions integration
  • Duplicate key handling
  • Input validation (empty, invalid, max dimensions)
  • Integration with existing Metrics features
  • High-resolution metrics compatibility

Quality:

• All existing tests pass (141 passed, 1 skipped, 0 failures)
• No regressions in existing functionality
• Code passes ruff formatting and mypy type checking

User experience

Before:
Users could only create metrics with a single dimension set, limiting their ability to aggregate metrics across different dimension combinations. To achieve hierarchical aggregation, users had to:

• Create multiple EphemeralMetrics instances (verbose, error-prone)
• Manually create separate EMF logs (excessive CloudWatch costs)
• Use CloudWatch Metric Math or SEARCH (complex, hard to maintain)
• Use third-party libraries like aws-embedded-metrics-python (additional dependencies)

# Previous workaround - verbose and creates multiple EMF objects
metrics1 = EphemeralMetrics()
metrics1.add_dimension(name="environment", value="prod")
metrics1.add_dimension(name="region", value="us-east-1")
metrics1.add_metric(name="SuccessfulRequests", unit=MetricUnit.Count, value=100)

metrics2 = EphemeralMetrics()
metrics2.add_dimension(name="environment", value="prod")
metrics2.add_metric(name="SuccessfulRequests", unit=MetricUnit.Count, value=100)

metrics3 = EphemeralMetrics()
metrics3.add_dimension(name="region", value="us-east-1")
metrics3.add_metric(name="SuccessfulRequests", unit=MetricUnit.Count, value=100)

After:
Users can now easily create multiple dimension sets within a single Metrics instance, enabling CloudWatch to automatically aggregate metrics across different dimension combinations. This creates cleaner code and more efficient metric ingestion:

from aws_lambda_powertools import Metrics
from aws_lambda_powertools.metrics import MetricUnit

metrics = Metrics(namespace="ServerlessAirline", service="booking")

@metrics.log_metrics
def lambda_handler(event, context):
    # Create dimension sets for different aggregation levels
    metrics.add_dimensions({"environment": "prod", "region": "us-east-1"})
    metrics.add_dimensions({"environment": "prod"})
    metrics.add_dimensions({"region": "us-east-1"})
    
    # Single metric value published with all dimension sets
    metrics.add_metric(name="SuccessfulRequests", unit=MetricUnit.Count, value=100)

EMF Output:
{
  "_aws": {
    "CloudWatchMetrics": [{
      "Namespace": "ServerlessAirline",
      "Dimensions": [
        ["service"],
        ["environment", "region"],
        ["environment"],
        ["region"]
      ],
      "Metrics": [{"Name": "SuccessfulRequests", "Unit": "Count"}]
    }]
  },
  "service": "booking",
  "environment": "prod",
  "region": "us-east-1",
  "SuccessfulRequests": 100.0
}

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[codecov]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 96.54%. Comparing base (c4631a9) to head (9035cff).
⚠️ Report is 1 commits behind head on develop.

Additional details and impacted files

@@           Coverage Diff            @@
##           develop    #7848   +/-   ##
========================================
  Coverage    96.53%   96.54%           
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  Files          275      275           
  Lines        13158    13194   +36     
  Branches       992      999    +7     
========================================
+ Hits         12702    12738   +36     
  Misses         353      353           
  Partials       103      103           

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[sonarqubecloud]

Quality Gate passed

Issues
0 New issues
0 Accepted issues

Measures
0 Security Hotspots
0.0% Coverage on New Code
0.0% Duplication on New Code

See analysis details on SonarQube Cloud

[leandrodamascena]

Hey @oyiz-michael! Thanks for this amazing PR! I just made some small changes and added docs and it's ready to merge