Date field inconsistently converted to datetime when used as a key in groupby
#### Code sample
```python
import pandas as pd
import numpy as np
df = pd.DataFrame(zip(np.random.rand(5).tolist(), [1]*5, [dt.date.today()]*5), columns=list('abc'))
print df
print df.groupby('c').a.apply(lambda x: x.max()).index
print df.groupby(['b', 'c']).a.apply(lambda x: x.max()).index.get_level_values(1)
print df.groupby(['b', 'c']).a.max().index.get_level_values(1)
```

The output is
```
    a   b   c
0   0.896739    1   2017-09-24
1   0.473168    1   2017-09-24
2   0.100591    1   2017-09-24
3   0.870899    1   2017-09-24
4   0.716934    1   2017-09-24
Index([2017-09-24], dtype='object', name=u'c')
DatetimeIndex(['2017-09-24'], dtype='datetime64[ns]', name=u'c', freq=None)
Index([2017-09-24], dtype='object', name=u'c')
```
#### Problem description

Out of the three group-by statements, date field (column 'c' in the above code) is converted from object data type to datetime64[ns] only in second statement. Shouldn't the date to datetime conversion be consistent in all three cases?

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

<details>

INSTALLED VERSIONS
------------------
commit: None
python: 2.7.10.final.0
python-bits: 64
OS: Darwin
OS-release: 16.7.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8
LOCALE: None.None

pandas: 0.19.2
nose: 1.3.7
pip: 9.0.1
setuptools: 18.5
Cython: 0.23.4
numpy: 1.12.1
scipy: 0.13.0b1
statsmodels: 0.8.0
xarray: None
IPython: 5.4.1
sphinx: None
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2017.2
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.1
openpyxl: 2.3.3
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.9.8
lxml: None
bs4: None
html5lib: 0.999999999
httplib2: 0.9.2
apiclient: 1.5.1
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.42.0
pandas_datareader: None

</details>
