fix(#543): coerce simple_bench CSV numeric columns; drop bad rows instead of crashing

[FileSystemGuy]

Fixes #543.

Summary

vdbbench.simple_bench.calculate_statistics() was crashing after the timed VectorDB benchmark had already completed:

```
TypeError: unsupported operand type(s) for +: 'float' and 'str'
at (all_data["timestamp"] + all_data["batch_time_seconds"]).max()
```

The reporter diagnosed the root cause: a small number of rows in two per-process CSVs (milvus_benchmark_p2.csv, milvus_benchmark_p8.csv) carried a batch_time_seconds value of the string True instead of a float duration. Once any value in a numeric column is non-numeric, pandas demotes the whole column to object dtype, and the arithmetic on (timestamp + batch_time_seconds).max() fails. The 60s benchmark completes, ground-truth runs, all 16 worker processes write their CSVs — then the wrapper marks the whole run as failed during the post-run statistics computation.

This PR hardens the reader so that result-data corruption in a handful of rows out of millions no longer destroys the whole run. Writer-side root-causing (why a row occasionally has shifted/missing fields) is left as a follow-up — the reader-side defense is enough to recover the benchmark result so long as the malformed rows are a small fraction.

Fix

In vdb_benchmark/vdbbench/simple_bench.py::calculate_statistics:

1. Tag every loaded row with its source CSV name so the diagnostic identifies affected workers.
2. Coerce timestamp, batch_size, batch_time_seconds, avg_query_time_seconds with pd.to_numeric(..., errors="coerce") before any arithmetic.
3. Identify rows where any required numeric column became NaN; log a clear warning naming the dropped count and affected file(s); drop those rows.
4. Continue with the remaining good rows. If every row was bad, fall through to the existing "No valid data found" error rather than raising.

The diagnostic line, when triggered, looks like:

```
Warning: dropping 2 row(s) with non-numeric values in required columns.
Affected file(s): milvus_benchmark_p2.csv, milvus_benchmark_p8.csv.
See #543.
```

Tests

New file: vdb_benchmark/tests/tests/test_issue_543_csv_coercion.py — 8 tests:

Test	What it covers
test_calculate_statistics_recovers_from_reporter_malformed_row	Exact 16-worker scenario from the issue body — 14 clean CSVs, 2 with one malformed row each in the reporter's exact shape (batch_time_seconds=True, leading + trailing columns empty). Asserts no crash and stats returned.
test_calculate_statistics_drops_rows_with_non_numeric_values[…] × 5	Parametrized — any one non-numeric value in any of the four numeric columns (timestamp, batch_time_seconds, batch_size, avg_query_time_seconds) must not crash.
test_calculate_statistics_returns_error_when_every_row_is_malformed	Edge case: all rows malformed returns the existing error key, not a raise.
test_calculate_statistics_unchanged_for_clean_csvs	Sanity: clean CSVs still produce a complete stats dict (happy-path regression check).

Test plan

• `pytest vdb_benchmark/tests/tests/test_issue_543_csv_coercion.py` — 8/8 pass
• `pytest vdb_benchmark/tests/` — 144/144 pass (no regressions in the wider vdb suite)

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