Unwanted conversion of `Timestamp` to `long`
Method `combine_first` performs an unwanted conversion of `Timestamp` to `long`. Pandas version is "0.11.0". Here is an IPython session demonstrating the behavior, which is IMHO a bug:

```
In [1]: import pandas as pd

In [2]: pd.__version__
Out[2]: '0.11.0'

In [4]: from datetime import datetime

In [5]: df0 = pd.DataFrame({"a":[datetime(2000, 1, 1), datetime(2000, 1, 2), datetime(2000, 1, 3)]})

In [6]: df0
Out[6]: 
                    a
0 2000-01-01 00:00:00
1 2000-01-02 00:00:00
2 2000-01-03 00:00:00

In [7]: df1 = pd.DataFrame({"a":[None, None, None]})

In [8]: df1
Out[8]: 
      a
0  None
1  None
2  None

In [9]: df2 = df1.combine_first(df0)

In [10]: df2
Out[10]: 
                    a
0  946684800000000000
1  946771200000000000
2  946857600000000000

In [11]: type(df2["a"][0])
Out[11]: long

In [12]: type(df0["a"][0])
Out[12]: pandas.tslib.Timestamp
```

I think `df2` should contain time stamps like `df0`.
