Add lakehouse Delta table detail APIs and over-partitioned table reporting

[Copilot]

This change adds first-class lakehouse APIs to surface Delta table size, file count, and partition metadata, and introduces a lakehouse-wide report for detecting over-partitioned tables. It addresses the need to quickly identify tables that may require partitioning/optimization adjustments using practical size thresholds.

• New Delta table detail API

  • Added get_delta_table_details(...) in sempy_labs.lakehouse._partitioning.
  • Returns table-level metadata needed for partition diagnostics:
    • Size In Bytes, Size In GB, Files
    • Partition Columns, Is Partitioned
    • Table Name, Schema Name

• New over-partitioning report API

  • Added list_over_partitioned_tables(...) to scan Delta tables in a lakehouse and return only those that breach thresholds.
  • Uses the requested heuristics:
    • total table size < 1 TB (default 1000 GB), or
    • average partition size < 1 GB.

• Partitioning logic consolidation

  • Refactored is_over_partitioned(...) to reuse shared detail-based evaluation logic.
  • Introduced shared constants for detail schema/typing to keep report output consistent, including empty results.

• Public lakehouse surface update

  • Exported partitioning/detail functions via sempy_labs.lakehouse:
    • is_partitioned
    • list_partitioned_columns
    • get_delta_table_details
    • is_over_partitioned
    • list_over_partitioned_tables

import sempy_labs.lakehouse as lakehouse

details = lakehouse.get_delta_table_details(
    table="fact_sales",
    schema="dbo",
    lakehouse="ContosoLakehouse",
    workspace="Finance"
)

over_partitioned = lakehouse.list_over_partitioned_tables(
    lakehouse="ContosoLakehouse",
    workspace="Finance",
    total_table_size_gb=1000,
    average_partition_size_gb=1
)