I added IndexedCoordinates.merge_coords so that it is easier to combine different coordinates to pass to a new Dataset / DataArray, e.g.,

```python midx = pd.MultiIndex.from_product([["a", "b"], [1, 2]], names=("one", "two"))

coords = xr.IndexedCoordinates.from_pandas_multiindex(midx, "x")

coords = coords.merge_coords({"y": [0, 1, 2]})

Coordinates:

* x (x) object MultiIndex

* one (x) object 'a' 'a' 'b' 'b'

* two (x) int64 1 2 1 2

* y (y) int64 0 1 2

ds = xr.Dataset(coords=coords)

<xarray.Dataset>

Dimensions: (x: 4)

Coordinates:

* x (x) object MultiIndex

* one (x) object 'a' 'a' 'b' 'b'

* two (x) int64 1 2 1 2

* y (y) int64 0 1 2

Data variables:

empty

```

IndexedCoordinates.merge_coords is very much like Coordinates.merge except that it returns a new Coordinates object instead of a Dataset.

Or should we just use merge? It would require that:

• Coordinates.merge accepts Mapping[Any, Any] for its other argument. Only changing the type hint is enough here since the implementation already accepts any input passed to Dataset.
• When a Dataset is passed as coords argument to a new Dataset and DataArray, both variables and indexes should be extracted. It is already the case for Dataset but I think it only works for PandasIndex and PandasMultiIndex (default indexes & backwards compatibility).