Support for globs

Is your feature request related to a problem?

When working with larger datasets or arrays with multiple dimensions, where the variable/dimension names are largely similar but differ only by, e.g., some appendix it would be nice to do operations using globs.

On top of that, often operations on datasets where, e.g., not all variables share some dimensions require additional loops over the variables.

In our code base we often have the construct of a dataset where each variable has unique dimensions that are similar in logic.
E.g. a variable called "lambda_nir" with a dimension "spectral_nir", and many more variables where the "_nir" appendix is replaced with different ones.

Then we often have to loop over variables to call apply_ufunc with the explicit dimension.
Here, it would be nice to also call apply_ufunc(..., input_core_dims=["spectral_*"], ...)

Describe the solution you'd like

e.g. instead of doing
ds[["img_a", "img_b", "img_c"]]
I would like to be able to do
ds["img_*"]

Similarly, I would like to be able to replace
ds.mean(["dim_a", "dim_b", "dim_c"])
by
ds.mean("dim_*")

Describe alternatives you've considered

In some operations you can already pass callables, but this is usually more work and less readable than just passing the list of dimensions.

Additional context

This feature is quite a breaking change, so would probably require a long deprecation phase and warn users when using names with * in them.

Start by reviewing the current dataset selection, reduction methods such as mean, and apply_ufunc handling described in the issue. Define how glob patterns should behave across these operations, including names containing '*', deprecation warnings, and cases where variables have different dimensions; done means the behavior and migration path are specified and covered consistently.