feat: test data generation

[rich-iannone]

This PR adds a test data generation system that creates realistic synthetic datasets from schema definitions. It supports typed field constraints (integers, floats, strings, booleans, dates), preset-based generation for realistic data like names, emails, and addresses, regex patterns for custom formats, and locale-aware data for 50 countries worldwide. Related fields maintain coherence as emails match names, and addresses have consistent city/postcode/coordinates.

Here's an example:

import pointblank as pb

schema = pb.Schema(
    user_id=pb.int_field(min_val=1, unique=True),
    name=pb.string_field(preset="name"),
    email=pb.string_field(preset="email"),
    age=pb.int_field(min_val=18, max_val=80),
    status=pb.string_field(allowed=["active", "pending", "inactive"]),
)

pb.preview(pb.generate_dataset(schema, n=100, seed=23))

For country-specific data:

schema = pb.Schema(
    name=pb.string_field(preset="name"),
    city=pb.string_field(preset="city"),
    address=pb.string_field(preset="address"),
)

pb.preview(pb.generate_dataset(schema, n=50, seed=23, country="DE"))