feat: add new aggregation checks and improve validation summary

[flitzpiepe93]

This merge request introduces several new aggregate-level data quality checks to the SparkDQ framework, along with a few documentation and type-handling improvements.

✅ New Features

• UniqueRowsCheck: Detects duplicate rows (optionally based on a subset of columns).
• UniqueRatioCheck: Checks if the fraction of distinct values in a column exceeds a defined threshold.
• CompletenessRatioCheck: Validates that a column has a sufficient non-null ratio.
• ColumnsAreCompleteCheck: Ensures a list of specified columns contains no nulls (all-or-nothing check).
• DistinctRatioCheck: Validates whether a column’s distinct-to-total ratio exceeds a specified minimum.

🛠 Improvements

• Added an all_checks_passed property to the ValidationSummary to simplify downstream evaluations.
• Added # type: ignore to ColumnsAreCompleteCheck to silence mypy due to a known type inference issue.
• Linked the Spark installation guide in the README.md for easier setup.

📚 Documentation

• Added a usage example link to the Sphinx documentation.

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Notes for Reviewers:

• All new checks are covered by unit tests.
• Each check integrates cleanly into the existing validation engine.
• Let me know if you prefer breaking this into smaller MRs.

[codecov]

Codecov Report

Attention: Patch coverage is 98.67550% with 2 lines in your changes missing coverage. Please review.

Files with missing lines	Patch %	Lines
...te/completeness_checks/completeness_ratio_check.py	93.33%	1 Missing and 1 partial ⚠️

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