BUG: comparing multicolumn dataframe with datetime64 values to series gives TypeError
When trying to compare a dataframe to a column/series (I know, in the following case not useful due to the alignement of the series with the columns of the dataframe and not the rows, but it is something typical users will try), I get the correct results if there are strings in the dataframe and series, but a TypeError when the dataframe contains datetime values:

```
In [1]: from io import StringIO

In [2]: s = """id       date birth_date_1 birth_date_2
   ...: 1 2000-01-01   2000-01-03   2000-01-05
   ...: 1 2000-01-07   2000-01-03   2000-01-05
   ...: 2 2000-01-02   2000-01-10   2000-01-01
   ...: 2 2000-01-05   2000-01-10   2000-01-01"""

In [3]: df = pd.read_csv(StringIO(s), sep='\s+')

In [5]: df[['birth_date_1','birth_date_2']] > df['date']
Out[5]:
       0      1      2      3 birth_date_1 birth_date_2
0  False  False  False  False         True         True
1  False  False  False  False         True         True
2  False  False  False  False         True         True
3  False  False  False  False         True         True

In [7]: df = pd.read_csv(StringIO(s), sep='\s+', parse_dates=[1,2,3])

In [8]: df[['birth_date_1','birth_date_2']] > df['date']
...
c:\users\vdbosscj\scipy\pandas-joris\pandas\core\internals.pyc in handle_error()

    954             if raise_on_error:
    955                 raise TypeError('Could not operate %s with block values
%s'
--> 956                                 % (repr(other), str(detail)))
    957             else:
    958                 # return the values

TypeError: Could not operate array(['2000-01-01T01:00:00.000000000+0100',
       '2000-01-07T01:00:00.000000000+0100',
       '2000-01-02T01:00:00.000000000+0100',
       '2000-01-05T01:00:00.000000000+0100'], dtype='datetime64[ns]') with block
 values invalid type promotion
```
