⚡ Bolt: [performance improvement] optimize DataFrame iteration in verify utility

[alinelena]

💡 What: Replaced slow df.iterrows() loop with C-optimized df.to_dict("records") when building cross-reference dictionaries for Parquet validation.
🎯 Why: df.iterrows() is an anti-pattern that creates a new Pandas Series object for every row, making iteration across large omol-25 datasets significantly slow.
📊 Impact: Converting to native Python dicts upfront and iterating them reduces overall processing overhead, improving speed and drastically decreasing memory allocation per row during iteration.
🔬 Measurement: Run python -m pytest tests/test_verify_processed_omol25.py and verify all tests pass smoothly. Optionally execute the utility on a large Parquet/XYZ pair and compare execution times.

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PR created automatically by Jules for task 13598759025420501430 started by @alinelena

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