feat: add new checks and floating api

[flitzpiepe93]

🚀 Add Fluent API and New Row-Level String Checks

This PR introduces major improvements to the sparkdq framework, including a fluent-style CheckSet API and a suite of reusable, declarative string validation checks.

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✨ What's New

✅ Fluent API for CheckSet

Checks can now be added using a chainable (floating) style:

check_set = (
    CheckSet()
    .add_check(...)
    .add_check(...)
)

🧪 New Row-Level Checks

• ColumnLessThanCheck
  Ensures that values in one column are strictly less than values in another (supports timestamps, dates, numerics)

• StringMinLengthCheck
  Validates that strings meet a minimum length (inclusive=True|False)

• StringMaxLengthCheck
  Ensures that string values do not exceed a maximum length

• StringLengthBetweenCheck
  Checks if string length lies within a specified [min, max] range

• RegexMatchCheck
  Flags values that do not match a given regex pattern
  Supports ignore_case and optional failure on nulls

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🔧 Other Improvements

• __init__.py now includes a __version__ for programmatic version access
• Simplified logic in StringMinLengthCheck for readability and consistency

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📚 Docs & Tests

• Full unit test coverage for all new checks
• Sphinx-ready documentation snippets (Python & YAML)
• Includes descriptions, config options, and use case examples

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✅ Checklist

• All new checks registered and documented
• Fully tested with null handling and edge cases
• Complies with Spark-native logic and framework architecture

[codecov]

Codecov Report

All modified and coverable lines are covered by tests ✅

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