BUG: tz-aware datetime with column-wise comparisions failing with np.minmum/maximum
#### Code Sample, a copy-pastable example if possible

```python
>>> import numpy as np
>>> import pandas as pd

>>> naive = pd.to_datetime(['now'])
>>> utc = naive.tz_localize('UTC')

>>> np.minimum(naive, naive)
>>> np.minimum(utc, utc)
>>> np.minimum(pd.Series(naive), pd.Series(naive))
>>> np.minimum(pd.Series(utc), pd.Series(utc))

  File "bug.py", line 10, in <module>
    np.minimum(pd.Series(utc), pd.Series(utc))
  File "~/anaconda3/lib/python3.5/site-packages/pandas/core/series.py", line 498, in __array_prepare__
    op=context[0].__name__))
TypeError: Series with dtype datetime64[ns, UTC] cannot perform the numpy op minimum

```
#### Problem description

When a tz-aware datetime is placed in a `Series`, the numpy operations `fmin`/`fmax`/`minimum`/`maximum` throw an error, even though these operations work fine on a tz-aware `DatetimeIndex`.

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

<details>

INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.9.8-100.fc24.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: C
LANG: C
LOCALE: None.None

pandas: 0.19.2
nose: 1.3.7
pip: 9.0.1
setuptools: 27.2.0
Cython: 0.25.2
numpy: 1.11.3
scipy: 0.18.1
statsmodels: 0.8.0
xarray: 0.9.1
IPython: 4.2.0
sphinx: 1.5.1
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: 1.2.0
tables: 3.3.0
numexpr: 2.6.2
matplotlib: 2.0.0
openpyxl: 2.4.1
xlrd: 1.0.0
xlwt: 1.2.0
xlsxwriter: 0.9.6
lxml: 3.7.2
bs4: 4.5.3
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: 1.1.5
pymysql: None
psycopg2: None
jinja2: 2.9.4
boto: 2.45.0
pandas_datareader: None
</details>
