map_over_datasets throws error on nodes without datasets

map_over_datasets -- a way to compute over datatrees -- currently seems to try an operate even on nodes which contain no datasets, and consequently raises an error.
This seems to be a new issue, and was not a problem when this function was called map_over_subtree, which was part of the experimental datatree versions.

An example to reproduce this problem is below:

## Generate datatree, using example from documentation
def time_stamps(n_samples, T):
    """Create an array of evenly-spaced time stamps"""
    return xr.DataArray(
        data=np.linspace(0, 2 * np.pi * T, n_samples), dims=["time"]
    )


def signal_generator(t, f, A, phase):
    """Generate an example electrical-like waveform"""
    return A * np.sin(f * t.data + phase)


time_stamps1 = time_stamps(n_samples=15, T=1.5)

time_stamps2 = time_stamps(n_samples=10, T=1.0)

voltages = xr.DataTree.from_dict(
    {
        "/oscilloscope1": xr.Dataset(
            {
                "potential": (
                    "time",
                    signal_generator(time_stamps1, f=2, A=1.2, phase=0.5),
                ),
                "current": (
                    "time",
                    signal_generator(time_stamps1, f=2, A=1.2, phase=1),
                ),
            },
            coords={"time": time_stamps1},
        ),
        "/oscilloscope2": xr.Dataset(
            {
                "potential": (
                    "time",
                    signal_generator(time_stamps2, f=1.6, A=1.6, phase=0.2),
                ),
                "current": (
                    "time",
                    signal_generator(time_stamps2, f=1.6, A=1.6, phase=0.7),
                ),
            },
            coords={"time": time_stamps2},
        ),
    }
)

## Write some function to add resistance
def add_resistance_only_do(dtree): 
    def calculate_resistance(ds):
        ds_new = ds.copy()
        
        ds_new['resistance'] = ds_new['potential']/ds_new['current']
        return ds_new 
        
    dtree = dtree.map_over_datasets(calculate_resistance)
    
    return dtree
    
def add_resistance_try(dtree): 
    def calculate_resistance(ds):
        ds_new = ds.copy()
        try:
            ds_new['resistance'] = ds_new['potential']/ds_new['current']
            return ds_new 
        except:
            return ds_new

    dtree = dtree.map_over_datasets(calculate_resistance)
    
    return dtree

Calling voltages = add_resistance_only_do(voltages) raises the error:

KeyError: "No variable named 'potential'. Variables on the dataset include []"
Raised whilst mapping function over node with path '.'

This can be easily resolved by putting try statements in (e.g. voltages = add_resistance_try(voltages)), but we know that Yoda would not recommend try (right @TomNicholas).

Can this be built in as a default feature of map_over_datasets? as many examples of datatree will have nodes without datasets.

Start at the map_over_datasets entry point and run the supplied DataTree reproduction, focusing on the root node with no dataset. Done means mapping skips nodes without datasets instead of raising the reported KeyError, while still applying the function to nodes that contain datasets.