BUG: Summing over multiple levels with MultiIndex CategoricalIndex introduces new columns
#### Code Sample, a copy-pastable example if possible

```python
import pandas as pd
mi = pd.MultiIndex.from_tuples([['b','d'],['b','c'],['b','c'],['a','c']], names=list('AB'))
mi = mi.set_levels(pd.CategoricalIndex(mi.levels[0], categories=['b','a']), level='A')
df = pd.DataFrame([[1,1,1,1],[1,1,1,1]], columns=mi)
df.sum(axis=1, level=[0,1])
```
returns:
```
A  b     a    
B  c  d  c   d
0  2  1  1 NaN
1  2  1  1 NaN
```

#### Problem description

A spurious column (a,d) containing all NaNs is included in the result. Also, the ordering of the second column level is no longer correct.

#### Expected Output

The same result as when level 'A' would not have been a CategoricalIndex:
```python
import pandas as pd
mi = pd.MultiIndex.from_tuples([['b','d'],['b','c'],['b','c'],['a','c']], names=list('AB'))
df = pd.DataFrame([[1,1,1,1],[1,1,1,1]], columns=mi)
df.sum(axis=1, level=[0,1])
````
which correctly returns:
```
A  b     a
B  d  c  c
0  1  2  1
1  1  2  1
```

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

<details>

INSTALLED VERSIONS
------------------
commit           : None
python           : 3.6.6.final.0
python-bits      : 64
OS               : Windows
OS-release       : 10
machine          : AMD64
processor        : Intel64 Family 6 Model 78 Stepping 3, GenuineIntel
byteorder        : little
LC_ALL           : None
LANG             : None
LOCALE           : None.None
pandas           : 0.25.1
numpy            : 1.17.1
pytz             : 2019.2
dateutil         : 2.8.0
pip              : 19.2.3
setuptools       : 39.0.1
Cython           : 0.29.4
pytest           : 4.2.1
hypothesis       : None
sphinx           : None
blosc            : None
feather          : None
xlsxwriter       : 1.1.2
lxml.etree       : 4.3.4
html5lib         : 1.0.1
pymysql          : None
psycopg2         : None
jinja2           : 2.10.1
IPython          : 5.8.0
pandas_datareader: None
bs4              : 4.6.1
bottleneck       : 1.2.1
fastparquet      : None
gcsfs            : None
lxml.etree       : 4.3.4
matplotlib       : 2.2.4
numexpr          : 2.6.9
odfpy            : None
openpyxl         : 2.5.12
pandas_gbq       : None
pyarrow          : None
pytables         : None
s3fs             : None
scipy            : 1.3.1
sqlalchemy       : 1.2.18
tables           : None
xarray           : 0.12.3
xlrd             : 1.2.0
xlwt             : 1.3.0
xlsxwriter       : 1.1.2

</details>
