BUG: DataFrame groupby values of a tz-aware datetime column returns underlying datetime integer
I am running into an issue which appears only in pandas 0.17 or higher where tz aware datetime columns get converted into their integer values by a groupby operation

``` python
## This is an issue when timestamps are TZ aware
dts = [pd.Timestamp('20160101', tz='UTC'), pd.Timestamp('20160102', tz='UTC')]
values = [1,2]
df = pd.DataFrame({'DATE':dts, 'VALUE':values})
df.groupby('VALUE').DATE.max()
Out[1]: 
VALUE
1    1451606400000000000
2    1451692800000000000
Name: DATE, dtype: int64

## This is not an issue when tmestamps are not TZ aware
dts = [pd.Timestamp('20160101'), pd.Timestamp('20160102')]
values = [1,2]
df = pd.DataFrame({'DATE':dts, 'VALUE':values})
df.groupby('VALUE').DATE.max()
Out[2]: 
VALUE
1   2016-01-01
2   2016-01-02
Name: DATE, dtype: datetime64[ns]

## Version information
pd.show_versions()

INSTALLED VERSIONS
------------------
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Linux
OS-release: 2.6.32-431.11.2.el6.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8

pandas: 0.17.1
nose: 1.3.7
pip: 7.1.2
setuptools: 19.1.1
Cython: 0.23.4
numpy: 1.10.2
scipy: 0.16.1
statsmodels: None
IPython: 4.0.1
sphinx: 1.3.1
patsy: 0.4.0
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.4.4
matplotlib: 1.5.0
openpyxl: 2.2.6
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.7.7
lxml: 3.5.0
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.10
pymysql: None
psycopg2: None
Jinja2: None

```
