add vectorized nearest method

[eeholmes]

Summary

Implement a new batch extraction method _extract_axis_batch() (or similar) for spatial_method="axis", replacing the per-point selection currently used for spatial_method="nearest" with fully vectorized indexing using xarray's .sel(..., method="nearest") on all points at once.

• new spatial_method="axis": Vectorized, 1-D lat/lon coordinate selection (xarray native axis-based nearest; works on regular grids) replaces spatial_method="nearest"
• spatial_method="auto": Now resolves as follows:
  • If both lat.ndim == 1 and lon.ndim == 1 → use axis
  • Else (either is 2D) → use euclidean

Detailed requirements

1. Vectorized extraction for axis:

   • See example in this notebook for how to implement vectorized indexing: how_to_select_xarray_gridcells_using_vectorized_indexing.ipynb
   • When spatial_method="axis" and a granule has one or more points, batch all points into a single .sel() call for all variables.
   • Support vectorized selection in latitude, longitude, and time (all in one call), as well as any extra coords supplied by coord_spec.
   • If the dataset lacks a time dimension but input points specify times, create a dummy/singleton time axis to enable vectorized selection.
   • Remove the old per-point nearest code and all spatial_method="nearest" references: spatial_method="axis" should always use the new batch/vectorized approach.
   • Check documentation in docs/ and examples/.py and examples/docs_.ipynb and README.md for any references to spatial_method="nearest" and replace with spatial_method="axis".

2. Additional axes:

   • For any additional axes specified in coord_spec (e.g., depth, wavelength), support as additional vectorized indexers.

3. auto logic update:

   • When spatial_method="auto", the engine should now check:
     • If both lat.ndim == 1 and lon.ndim == 1 in the dataset, use axis (vectorized via 1-D axis matching)
     • Else, use existing logic to switch to a 2D method
     • it would be very unusual for lat to be 1D and lon to be 2D, but don't fail if that were the case. Make into 2D and 2D method.

4. Quality and compatibility:

   • Keep fallback/error handling as now (empty slices, NaNs, etc).
   • API should remain: result rows, per-variable expansion, NaN handling, etc.
   • Unit tests should cover:
     • Datasets with/without time coordinates
     • With/without extra axes
     • Few/many points per granule
     • 1-D vs 2-D coordinate arrays

Example of vectorized indexing

df = pd.DataFrame({
    'latitude': [51.5072, 48.8566, 40.7128, 35.6762],
    'longitude': [0.1276, 2.3522, 74.0060, 139.6503],
},
    index = [1, 2, 3, 4],
                 )
df.index.name = 'points'

ds_pts = df.to_xarray()
ptime = xr.DataArray(['2023-08-09'], dims=['time']) # note, this is probably not best way to do the time indexing since time will be in points dataframe for points

da.sel(time=ptime, latitude=ds_pts.latitude, longitude=ds_pts.longitude, method='nearest')