BUG: reset_index of level on a MultiIndex with NaT converts to np.nan
Not sure if it's know already, but couldn't find any open issues.
It's similar to this closed one:
https://github.com/pydata/pandas/issues/10388

using pandas 0.17 and numpy 1.10.1

code to reproduce the issues:

```
import pandas as pd
from pandas import DataFrame
import numpy as np

idx = np.arange(0, 10)  # could have an NaN?
tstamp = pd.date_range('201507010000', freq='h', periods=10).values
df = DataFrame({'id': idx, 'tstamp': tstamp, 'a': list('abcdefghij')})
df.loc[3, 'tstamp'] = pd.NaT

print 'without timezone:'
try:
    a = df.set_index(['id', 'tstamp']).reset_index('tstamp')
    print 'a works'
except Exception as e:
    print 'a fails: %s: %s' %(e.__class__.__name__, e)
try:
    b = df.set_index(['id', 'tstamp']).reset_index('tstamp').reset_index('id')
    print 'b works'
except Exception as e:
    print 'b fails: %s: %s' %(e.__class__.__name__, e)
try:
    c =  df.set_index(['id', 'tstamp']).reset_index()
    print 'c works'
except Exception as e:
    print 'c fails: %s: %s' %(e.__class__.__name__, e)
try:
    d =  df.set_index(['id', 'tstamp']).reset_index('id')
    print 'd works'
except Exception as e:
    print 'd fails: %s: %s' %(e.__class__.__name__, e)
print 'with timezone:'
df['tstamp'] = pd.DatetimeIndex(df['tstamp']).tz_localize('Europe/Berlin')
try:
    a = df.set_index(['id', 'tstamp']).reset_index('tstamp')
    print 'a works'
except Exception as e:
    print 'a fails: %s: %s' %(e.__class__.__name__, e)
try:
    b = df.set_index(['id', 'tstamp']).reset_index('tstamp').reset_index('id')
    print 'b works'
except Exception as e:
    print 'b fails: %s: %s' %(e.__class__.__name__, e)
try:
    c =  df.set_index(['id', 'tstamp']).reset_index()
    print 'c works'
except Exception as e:
    print 'c fails: %s: %s' %(e.__class__.__name__, e)
try:
    d =  df.set_index(['id', 'tstamp']).reset_index('id')
    print 'd works'
except Exception as e:
    print 'd fails: %s: %s' %(e.__class__.__name__, e)
```

Output:
without timezone:
a works
b works
c works
d fails: ValueError: Could not convert object to NumPy datetime
with timezone:
a fails: TypeError: data type not understood
b fails: TypeError: data type not understood
c fails: TypeError: data type not understood
d fails: ValueError: Could not convert object to NumPy datetime

Can you suggest any workaround?
