Is your feature request related to a problem?

When a netcdf file contains netcdf4 enums , xarray ignores the underlying enum type. The association between the values of the variable and their actual meaning is then lost.

MRE: ```py import netCDF4 as nc import xarray as xr

-- Create dataset with an enum using the netcdf4 lib

ds = nc.Dataset("mre.nc", "w", format="NETCDF4")
cloud_type_enum = ds.createEnumType(int,"cloud_type",{"clear":0, "cloudy":1}) print(ds.enumtypes)

{'cloud_type': <class 'netCDF4._netCDF4.EnumType'>: name = 'cloud_type', numpy dtype = int64, fields/values ={'clear': 0, 'cloudy': 1}}

ds.createVariable("cloud", cloud_type_enum) ds["cloud"][0] = 1 ds.close()

-- Open dataset with xarray

xr_ds = xr.open_dataset("./mre.nc") print(xr_ds.cloud)

<xarray.DataArray 'cloud' ()> \n [1 values with dtype=int64]

--> We get no metadata about the cloud_type enum that we created above

xr.ds.to_netcdf("mre_xr.nc")

-- Open xarray outputted dataset with netCDF4 lib

print(nc.Dataset("mre_xr.nc", "r", format="NETCDF4").enumtypes())

{}

--> Empty dictionary: the enum we created is lost

```

If you know CF, enums could replace replace flag_meanings and flag_values, see CF Enums are not yet part of CF though.

Describe the solution you'd like

As far as I understand, to describe the enum we only need a dictionary that map numbers (enum key) to string (enum value) and a way to reference this dictionary in variables that are "typed" to this enum. Bear in mind that the dtype of the variable would still be a number, the enum type would be a secondary metadata.

Describe alternatives you've considered

Most people that produce data could get away with using flag_meanings and flag_values to describe their data in a way which is both CF proof and properly managed by xarray. For me, the only workaround at the moment is to use the netCDF4 library directly.

Additional context

```py nc.version

1.6.2

xr.version

2023.2.0

```