Zarr reader - #271

WIP PR to add a Zarr reader. Thanks to @TomNicholas for the how to write a reader guide.

• Closes Add Zarr Reader(s) #262
• Tests added
• Tests passing
• Full type hint coverage
• Changes are documented in docs/releases.rst
• optimizations (e.g. using async interface to list lengths of chunks for each variable concurrently)
• New functionality has documentation
• Read v3 Zarr

Future PR(s):

• Read v2 Zarr
• sharded v3 data

To Do:

• Open the store using zarr-python v3 (behind a protected import). This should handle both v2 and v3 stores for us.
• Use zarr-python to list the variables in the store, and check that all loadable_variables are present
  For each virtual variable:
• Use zarr-python to get the attributes and the dimension names, and coordinate names (which come from the .zmetadata or zarr.json)
• Use zarr-python to also get the dtype and chunk grid info + everything else needed to create the virtualizarr.zarr.ZArray object (eventually we can skip this step and use a zarr-python array metadata class directly instead of virtualizarr.zarr.ZArray, see
  Replace VirtualiZarr.ZArray with zarr ArrayMetadata #175)
• Use the knowledge of the store location, variable name, and the zarr format to deduce which directory / S3 prefix the chunks must live in.
• List all the chunks in that directory using fsspec.ls(detail=True), as that should also return the nbytes of each chunk. Remember that chunks are allowed to be missing.
• The offset of each chunk is just 0 (ignoring sharding for now), and the length is the file size fsspec returned. The paths are just all the paths fsspec listed.
• Parse the path and length information returned by fsspec into the structure that we can pass to ChunkManifest.init
• Create a ManifestArray from our ChunkManifest and ZArray
• Wrap that ManifestArray in an xarray.Variable, using the dims and attrs we read before
  Get the loadable_variables by just using xr.open_zarr on the same store (should use drop_variables to avoid handling the virtual variables that we already have).
• Use separate_coords to set the correct variables as coordinate variables (and avoid building indexes whilst doing it)
• Merge all the variables into one xr.Dataset and return it.
• All the above should be wrapped in a virtualizarr.readers.zarr.open_virtual_dataset function, which then should be called as a method from a ZarrVirtualBackend(VirtualBackend) subclass.
• Finally add that ZarrVirtualBackend to the list of readers in virtualizarr.backend.py