append : NaT should be the default for missing values for datetime64 columns
Hello,

Here is a issue I discovered in pandas version '0.12.0' (already present in preview version I think)

Issue:
When appending a DataFrame (with a new datetime64 column) to an existing one, the default value for missing values should be a pandas.tslib.NaT.

Example:

``` python
import pandas as pd
import datetime as dt
from pandas.tslib import NaT
df1 = pd.DataFrame(index=[1,2],\
    data=[dt.datetime(2013,1,1,0,0),dt.datetime(2013,1,2,0,0)],\
    columns=['start_time'])
```

![df1](https://f.cloud.github.com/assets/1043822/1216925/e9bfa8a2-2696-11e3-8098-d7c1d902c6a5.jpg)

``` python
df2 = pd.DataFrame(index=[4,5],\
    data=[[dt.datetime(2013,1,3,0,0),dt.datetime(2013,1,3,6,10)],[dt.datetime(2013,1,4,0,0),dt.datetime(2013,1,4,7,10)]],\
    columns=['start_time','end_time'])
```

![df2](https://f.cloud.github.com/assets/1043822/1216942/5a2acd24-2697-11e3-8552-c3ee2089a451.jpg)

``` python
df3=df1.append(df2,ignore_index=True)
```

![df3_nan](https://f.cloud.github.com/assets/1043822/1216943/5ce3de66-2697-11e3-97a6-1e553fbb9677.jpg)

While in reallity we want: 
![df3_wanted](https://f.cloud.github.com/assets/1043822/1216944/5eabcf7e-2697-11e3-9505-f5b24baa7208.jpg)

A simple work around is:

``` python
df3['end_time']=df3['end_time'].apply(pd.to_datetime)
```

Could be nice if, be default, when a new "datetime64" column is added, the default for missing values is NaT. Otherwise this creates problem when, for example, saving as HDF5 using pytable which does not accept mixed types per column.

Have a nice day,

Patrick
