Bitwise operations have inconsistent behavior, different from numpy
#### Code Sample

```python
# succeeds
pd.Series([False]) & pd.Series([6.])

# Example: order matters
# fails: ufunc 'bitwise_and' not supported for the input types
pd.Series([6.]) & pd.Series([False])

# Example: behavior is different from numpy
# fails: ufunc 'bitwise_and' not supported for the input types
np.array([False]) & np.array([6.])


```
#### Problem description
Bitwise operations between floats and bools error out in numpy. They error out in pandas too if the first argument is a `float`, but not if the first argument is a `bool`. 

#### Expected Output
ufunc 'bitwise_and' not supported for the input types

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

<details>

INSTALLED VERSIONS
------------------
commit: None
python: 3.6.3.final.0
python-bits: 64
OS: Darwin
OS-release: 17.7.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8

pandas: 0.23.4
pytest: None
pip: 18.0
setuptools: 36.5.0.post20170921
Cython: 0.28.2
numpy: 1.15.2
scipy: 1.1.0
pyarrow: None
xarray: None
IPython: 6.1.0
sphinx: None
patsy: 0.5.0
dateutil: 2.6.1
pytz: 2017.2
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 2.2.2
openpyxl: None
xlrd: 1.1.0
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: 0.999999999
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.9.6
s3fs: 0.1.5
fastparquet: None
pandas_gbq: None
pandas_datareader: None
</details>
