Regression in .loc with missing key on Categorical axis
#### Code Sample, a copy-pastable example if possible

```python
In [2]: df = pd.DataFrame(-1, index=range(3), columns=list('abcd'))

In [3]: df.loc[:, list('abe')]
/home/nobackup/repo/pandas/pandas/core/indexing.py:1504: FutureWarning: 
Passing list-likes to .loc or [] with any missing label will raise
KeyError in the future, you can use .reindex() as an alternative.

See the documentation here:
https://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate-loc-reindex-listlike
  return self._getitem_tuple(key)
Out[3]: 
   a  b   e
0 -1 -1 NaN
1 -1 -1 NaN
2 -1 -1 NaN

In [4]: df.columns = df.columns.astype('category')

In [5]: df.loc[:, list('abe')]
---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
<ipython-input-5-f046079c7cb9> in <module>()
----> 1 df.loc[:, list('abe')]

/home/nobackup/repo/pandas/pandas/core/indexing.py in __getitem__(self, key)
   1502             except (KeyError, IndexError):
   1503                 pass
-> 1504             return self._getitem_tuple(key)
   1505         else:
   1506             # we by definition only have the 0th axis

/home/nobackup/repo/pandas/pandas/core/indexing.py in _getitem_tuple(self, tup)
    894                 continue
    895 
--> 896             retval = getattr(retval, self.name)._getitem_axis(key, axis=i)
    897 
    898         return retval

/home/nobackup/repo/pandas/pandas/core/indexing.py in _getitem_axis(self, key, axis)
   1910                     raise ValueError('Cannot index with multidimensional key')
   1911 
-> 1912                 return self._getitem_iterable(key, axis=axis)
   1913 
   1914             # nested tuple slicing

/home/nobackup/repo/pandas/pandas/core/indexing.py in _getitem_iterable(self, key, axis)
   1211             # A collection of keys
   1212             keyarr, indexer = self._get_listlike_indexer(key, axis,
-> 1213                                                          raise_missing=False)
   1214             return self.obj._reindex_with_indexers({axis: [keyarr, indexer]},
   1215                                                    copy=True, allow_dups=True)

/home/nobackup/repo/pandas/pandas/core/indexing.py in _get_listlike_indexer(self, key, axis, raise_missing)
   1148         # if it cannot handle:
   1149         indexer, keyarr = ax._convert_listlike_indexer(key,
-> 1150                                                        kind=self.name)
   1151         # We only act on all found values:
   1152         if indexer is not None and (indexer != -1).all():

/home/nobackup/repo/pandas/pandas/core/indexes/base.py in _convert_listlike_indexer(self, keyarr, kind)
   1670             keyarr = self._convert_arr_indexer(keyarr)
   1671 
-> 1672         indexer = self._convert_list_indexer(keyarr, kind=kind)
   1673         return indexer, keyarr
   1674 

/home/nobackup/repo/pandas/pandas/core/indexes/category.py in _convert_list_indexer(self, keyarr, kind)
    622         if (indexer == -1).any():
    623             raise KeyError(
--> 624                 "a list-indexer must only "
    625                 "include values that are "
    626                 "in the categories")

KeyError: 'a list-indexer must only include values that are in the categories'
```

#### Problem description

I don't have time to bisect now, but I guess this is due to #21569 (or alternatively #21594).

However I don't think the fix should be (another special case) in ``core/indexing.py``, but rather in ``Categorical`` code, which once more is artificially limiting the ability of interfacing with missing categories, something we should avoid at least in the internals.

(More in general, it is annoying that a specific kind of index performs, on given - public and private - methods, differently from all other kinds)

#### Expected Output

``Out[3]:``

(Unless we decide that our deprecation cycle for missing keys in ``.loc`` immediately affects ``Categorical`` axes, and document this...)

#### Output of ``pd.show_versions()``

<details>

INSTALLED VERSIONS
------------------
commit: dc45fbafef172e357cb5decdeab22de67160f5b7
python: 3.5.3.final.0
python-bits: 64
OS: Linux
OS-release: 4.9.0-6-amd64
machine: x86_64
processor: 
byteorder: little
LC_ALL: None
LANG: it_IT.UTF-8
LOCALE: it_IT.UTF-8

pandas: 0.24.0.dev0+193.gdc45fbafe
pytest: 3.5.0
pip: 9.0.1
setuptools: 39.2.0
Cython: 0.25.2
numpy: 1.14.3
scipy: 0.19.0
pyarrow: None
xarray: None
IPython: 6.2.1
sphinx: 1.5.6
patsy: 0.5.0
dateutil: 2.7.3
pytz: 2018.4
blosc: None
bottleneck: 1.2.0dev
tables: 3.3.0
numexpr: 2.6.1
feather: 0.3.1
matplotlib: 2.2.2.post1153+gff6786446
openpyxl: 2.3.0
xlrd: 1.0.0
xlwt: 1.3.0
xlsxwriter: 0.9.6
lxml: 4.1.1
bs4: 4.5.3
html5lib: 0.999999999
sqlalchemy: 1.0.15
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: 0.2.1
gcsfs: None

</details>
