[Python] Support tree-model TsFiles in TsFileDataFrame

[Young-Leo]

Summary

This PR teaches the Python TsFileDataFrame to read tree-model TsFiles
(in addition to the existing table-model support) and, in the same series of
commits, slims the underlying dataset index so it scales to wide / sparse
schemas without paying for phantom (device, field) cells.

The user-facing dataset surface (__len__, list_timeseries, __getitem__,
.loc, aligned reads) is unchanged for both models — tree-model files
just become loadable through the same API.

What changed

Tree-model support

• Detect the model kind at reader open. An empty table-schema map ⇒ tree
  model; otherwise table model.
• For tree files, synthesize one virtual TableEntry:
  • table name = the shared root segment of every device path
  • tag columns = _col_1 .. _col_N (one positional column per remaining
    path segment, padded with None for shorter devices)
  • fields = union of measurements across all devices
• Per-device measurement ownership is preserved by registering only the
  (device_id, field_idx) pairs that are actually written on disk in
  series_stats_by_ref, so the dataset never advertises phantom series.
• Tree-model reads route through query_table_on_tree with client-side
  device filtering. This works around two cwrapper limitations on the
  current native build (query_tree_by_row rejects multi-segment device
  paths, and successive query_table_on_tree calls leak duplicate col_*
  columns); both are documented inline at the cwrapper boundary.
• Tree-mode rendering: drop the leading table column, use _col_i
  headers, print None tag cells as "None", and surface the model
  marker in the repr header.
• Mixing table-model and tree-model files in one load set is rejected
  with a clear error.

Dataset index slim-down

• SeriesStats becomes a NamedTuple (~120 B vs. the previous ~360 B
  per-series dict).
• _DerivedCache removed; lookups computed lazily on top of existing
  state.
• Per-reader device_refs: List[List[DeviceRef]] collapsed into a
  pre-aggregated device_time_bounds: List[Tuple[Optional[int], Optional[int]]], so _query_aligned reads bounds in O(1).
• Drop the redundant series_ref_set (use series_ref_map keys).
• Phase 6: unify table/tree semantics so the table-model branch no
  longer pads series_stats_by_ref with empty placeholders for
  schema-declared but never-written cells. The dataset view is now
  strictly “real devices × real fields” in both models.
• Cleanup: rename _LogicalIndex → _DataFrameCatalog, shorten the five
  internal field names (devices / device_index / device_time_bounds
  / series / series_shards), inline the now-trivial
  iter_owned_series_refs wrapper.

New public API

• TsFileDataFrame.model — read-only model marker ("table" or "tree").
• TsFileDataFrame.list_timeseries_metadata() — per-series metadata as a
  flat tabular view (works identically for both models).

Compatibility

• No changes to the existing dataset surface. Existing user code that
  loads table-model TsFiles continues to work without modification.
• No changes to the on-disk format, the cwrapper, or the C++/Java sides.
• SeriesStats integer fields tighten from Optional[int] to int. The
  surrounding get_series_info_by_ref still exposes them as the existing
  dict shape, so callers do not see an API change.

Memory impact

Two benches were used because the wins land in different shapes of
schema.

Bench A — 30 k devices × 1 field, full density

Step	Tracked size	Δ
baseline	81.20 MB	—
SeriesStats NamedTuple	70.67 MB	−10.53 MB
_DerivedCache removal	59.82 MB	−10.85 MB
device_time_bounds aggregation	56.40 MB	−3.42 MB
series_ref_set removal	54.40 MB	−2.00 MB
drop phantom (device, field) cells	54.40 MB	0

End-to-end: 81.20 → 54.40 MB (−33 %). Dropping phantom cells brings
nothing here because every device writes the single declared field;
there are no skipped cells to prune.

Bench B — 5 k devices × 5 fields × 60 % density (sparse / wide)

Component	Before	After
series_ref_map	15.93 MB	9.55 MB
series_stats_by_ref	7.53 MB	4.51 MB
tracked total	26.50 MB	16.30 MB

Dropping phantom cells alone brings −38 % on this fixture; the
sparser and wider the schema, the larger the win.

Testing

• python -m pytest python/tests/test_tsfile_dataset.py → 41 / 41 pass.
• Four new tree-model tests cover: metadata + repr layout, single-series
  read, multi-field aligned read, list_timeseries_metadata column
  shape, and the mixed-model load rejection.
• One new sparse-schema test (test_dataset_omits_table_model_phantom_series_for_skipped_cells)
  proves Tablet-skipped cells stay out of list_timeseries, __len__,
  series_ref_map, and that tsdf[skipped_path] raises KeyError.