Incoherent behavior when ambiguously indexing MultiIndexed DataFrame with slice or list
Sorry in advance if this is already discussed/reported - I searched in the archive, but didn't know exactly what to search.

#### Code Sample, a copy-pastable example if possible

```python
In [2]: tt = pd.DataFrame([[1, 2, 'v1', 'v2'], [3, 4, 'v3','v4']],
   ...:                   columns=['idx1', 'idx2', 2, 6]).set_index(['idx1', 'idx2'])

In [3]: tt
Out[3]: 
            2   6
idx1 idx2        
1    2     v1  v2
3    4     v3  v4

In [4]: tt.loc[1,2]
Out[4]: 
2    v1
6    v2
Name: (1, 2), dtype: object

In [5]: tt.loc[:1,2]
Out[5]: 
idx1  idx2
1     2       v1
Name: 2, dtype: object

In [6]: tt.loc[:,2]
Out[6]: 
idx1  idx2
1     2       v1
3     4       v3
Name: 2, dtype: object

```
#### Problem description

``.loc[l1, l2]`` called on a ``MultiIndex``ed ``DataFrame`` is ambiguous: ``l2`` could refer to the second level of the ``index``, or to the ``columns``. Apparently, the decision has been taken to follow the first interpretation, and it is fine. But then, the same must happen when ``l1`` and ``l2`` are slices.

I can understand that ``In [6]`` might "look different" from ``In [4]``: but ``In [4]`` and ``In [5]`` should really give the same result (and hence ``In [6]`` too).

#### Expected Output

``Out [4]`` in all three cases (or ``Out [6]`` if we prefer to favour the second interpretation - which however would probably be more disruptive).

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

<details>

INSTALLED VERSIONS
------------------
commit: None
python: 3.5.3.final.0
python-bits: 64
OS: Linux
OS-release: 4.7.0-1-amd64
machine: x86_64
processor: 
byteorder: little
LC_ALL: None
LANG: it_IT.utf8
LOCALE: it_IT.UTF-8

pandas: 0.20.1
pytest: 3.0.6
pip: 9.0.1
setuptools: None
Cython: 0.25.2
numpy: 1.12.1
scipy: 0.19.0
xarray: 0.9.2
IPython: 5.1.0.dev
sphinx: 1.5.6
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2017.2
blosc: None
bottleneck: 1.2.1
tables: 3.3.0
numexpr: 2.6.1
feather: 0.3.1
matplotlib: 2.0.2
openpyxl: 2.3.0
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: 0.9.6
lxml: 3.7.1
bs4: 4.5.3
html5lib: 0.999999999
sqlalchemy: 1.0.15
pymysql: None
psycopg2: None
jinja2: 2.9.6
s3fs: None
pandas_gbq: None
pandas_datareader: 0.2.1


</details>
