API: NaT/nan mixed input coerces to timedelta64
#### Code Sample, a copy-pastable example if possible

`Index`/`Series` results in `datetime64` dtype if all inputs are `pd.NaT`.

```
pd.Index([pd.NaT, pd.NaT])
# DatetimeIndex(['NaT', 'NaT'], dtype='datetime64[ns]', freq=None)

pd.Series([pd.NaT, pd.NaT])
#0   NaT
#1   NaT
# dtype: datetime64[ns]
```

However, it coerces to `timedelta64` if `np.nan` is included. 

```
pd.Index([pd.NaT, np.nan])
# TimedeltaIndex([NaT, NaT], dtype='timedelta64[ns]', freq=None)

pd.Series([pd.NaT, np.nan])
#0   NaT
#1   NaT
# dtype: timedelta64[ns]
```

This looks to be caused by following internals:

```
pd.tslib.is_timestamp_array(np.array([pd.NaT, np.nan]))
# False
pd.lib.is_timedelta_array(np.array([pd.NaT, np.nan]))
# True
```
#### Expected Output

`datetime64` dtype?
#### output of `pd.show_versions()`

on current master
