Rerun parquet ingestion

ParquetReader is a pure reader: it maps a flat table onto the Rerun model by turning raw columns into grouped, time-indexed chunks of struct and scalar components. Column-name prefixes become entities, grouped columns become a single struct component, designated columns become timelines. The reader does not assemble archetypes anymore — mapping struct fields into typed Rerun components (Transform3D, Scalars, Points3D) is done with lenses on the reader's .stream(). The whole reader job is configuration; fill in the rerun-data-model mapping table first, then express it through stream(). Stream mechanics after .stream() are in rerun-chunk-processing.

The whole table is configuration, not code. If you find yourself building Chunk.from_columns from a parquet, or munging it in pandas first, stop — ParquetReader plus a lens almost certainly expresses it. Anything the reader cannot express (per-row entity routing, derived values, unit conversion) belongs in lenses downstream, not in pre-pandas munging; keep the pipeline columnar.