Inconsistent handling of duplicate axes + mixed dtypes in `DataFrame.where`
#### Code Sample, a copy-pastable example if possible

```python
result = pd.DataFrame([
    [0, np.nan]
], columns=pd.Index(['A', 'A']))

mask = pd.DataFrame([[True, True]], *result.axes)

a = result.astype(object).where(mask) # works
b = result.astype('f8').where(mask) # works
c = result.T.where(mask.T).T # works
d = result.where(mask) # fails: "cannot reindex from a duplicate axis"
```

#### Problem description

It doesn't make sense that `a`, `b` and `c` work but `d` doesn't. The dtype of a column shouldn't affect whether or not masking suceeds.

#### Expected Output

`a.astype('f8').equals(b.astype('f8')) and b.astype('f8').equals(c.astype('f8')) and c.astype('f8').equals(d.astype('f8'))`

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

<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.1.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 63 Stepping 2, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None

pandas: 0.24.1
pytest: 3.1.2
pip: 19.0.2
setuptools: 39.0.1
Cython: 0.27.2
numpy: 1.16.1
scipy: 1.2.1
pyarrow: 0.9.0
xarray: None
IPython: 6.1.0
sphinx: None
patsy: 0.4.1
dateutil: 2.8.0
pytz: 2018.9
blosc: None
bottleneck: 1.3.0.dev0
tables: 3.4.4
numexpr: 2.6.9
feather: None
matplotlib: 2.2.2
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: 3.8.0
bs4: 4.6.0
html5lib: 0.9999999
sqlalchemy: 1.1.11
pymysql: None
psycopg2: None
jinja2: 2.9.6
s3fs: None
fastparquet: 0.1.5
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
