BUG: Groupby NaT Handling
xref #9236 

There seems to be an inconsistency in some GroupBy methods when `NaT` is included in the group key.
- `GroupBy.groups` includes `NaT` as a key.
- `GroupBy.ngroups` doesn't count `NaT`.
- `GroupBy.__iter__` doesn't return `NaT` group.
- `GroupBy.get_group` fails when `NaT` is specified.

I understand `NaT` should be included in the group key according to other function's behaviour, such as `dropna`. Is it OK to fix it to include `NaT`?

```
import pandas as pd
import numpy as np
>>> df = pd.DataFrame({'values': np.random.randn(8), 
                   'dt': [np.nan, pd.Timestamp('2013-01-01'), np.nan, pd.Timestamp('2013-02-01'),
                          np.nan, pd.Timestamp('2013-02-01'), np.nan, pd.Timestamp('2013-01-01')]})
>>> grouped = df.groupby('dt')

>>> grouped.groups
{numpy.datetime64('NaT'): [0, 2, 4, 6], numpy.datetime64('2013-01-01T09:00:00.000000000+0900'): [1, 7], numpy.datetime64('2013-02-01T09:00:00.000000000+0900'): [3, 5]}

>>> grouped.ngroups
2

>>> grouped.indices
# ValueError: DatetimeIndex with NaT cannot be converted to object

>>> grouped.get_group(pd.NaT)
ValueError: DatetimeIndex with NaT cannot be converted to object
```
