Constrained loading fails if more than one variable in a file

```
var_con = iris.Constraint(cube_func=(lambda c: c.var_name == 'vo'))
vo = ch.load(['nemo_ay652o_1m_19500101-19500201_grid-V.nc'], constraints=var_con)
```

fails with

```
ConstraintMismatchError: failed to merge into a single cube.
cube.long_name differs: 'VS' != 'VV'
cube.var_name differs: 'vso' != 'v2o'
cube.units differs: Unit('unknown') != Unit('m/s')
cube.attributes keys differ: 'invalid_units'
```

because in load_from_filelist() line 283:

``` 283 if not _constraint_compatible(constraint,
--> 284 iris.load_cube(paths[0])):
```

`iris.load_cube()` will fail because there are multiple variables in the file. A new test should be introduced that contains multiple variables in a file. This isn't like CMIP6 data, but many users outside of CMIP6 analysis could work like this.

Start at load_from_filelist() around line 283 and reproduce the constrained load with the example file containing multiple variables. Add a test fixture or case with multiple variables in one file, then verify that selecting the requested variable succeeds without the reported ConstraintMismatchError.

Written by the indexing model from the issue text.