Using .loc.__setitem__ with integer slices does not raise
#### Code Sample

```python
>>> d = pd.DataFrame({'a': range(5), 'b': range(2,7), 'c': range(3,8)})
>>> d
   a  b  c
0  0  2  3
1  1  3  4
2  2  4  5
3  3  5  6
4  4  6  7
>>> d.loc[0:2, 0:2] = 42   # note that loc is used with an integer slice! should this raise?
>>> d  # this is somehow the expected result
    a   b  c
0  42  42  3
1  42  42  4
2  42  42  5
3   3   5  6
4   4   6  7
>>> d.iloc[0:2, 0:2]        # expected result
    a   b
0  42  42
1  42  42
>>> d.loc[0:2, 0:2]        # this is the same syntax as above, but raises
...
TypeError: cannot do slice indexing on <class 'pandas.core.indexes.base.Index'> with these indexers [0] of <class 'int'>
```
#### Problem description

I'd expect a symmetry in `d.loc.__getitem__` and `d.loc.__setitem__`. At the moment only __getitem__ raises if integer slices are passed. Usage of `.loc` was probably a mistake, instead `.iloc` should have been used. Even if it works correctly, it is confusing for users.

Probably this is simply a missing exception in the special case of `d.loc.__setitem__` being called with integer slices. 

See #13831 for a very similar problems with `pd.Series`.

#### Expected Output

Assuming `d.loc.__getitem__` should raise for integer slices (as it does), then also `d.loc.__setitem__` should raise:

```
>>> d.loc[0:2, 0:2]
...
SomException: int & loc don't go together
```

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

<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.8.final.0
python-bits: 64
OS: Linux
OS-release: 4.18.0-20-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8

pandas: 0.24.2
pytest: 3.6.4
pip: 9.0.1
setuptools: 40.8.0
Cython: None
numpy: 1.16.2
scipy: 1.2.1
pyarrow: None
xarray: 0.11.3
IPython: 5.5.0
sphinx: 1.7.9
patsy: 0.4.1+dev
dateutil: 2.8.0
pytz: 2019.1
blosc: 1.5.1
bottleneck: 1.2.1
tables: 3.4.4
numexpr: 2.6.5
feather: None
matplotlib: 2.2.2
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: 4.2.5
bs4: 4.5.0
html5lib: 0.999999999
sqlalchemy: None
pymysql: None
psycopg2: 2.7.5 (dt dec pq3 ext lo64)
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>