Error in Timezone handling of DataFrames when joined
When joining two DataFrames with localized indexes, the resulting DataFrame has erroneous Timestamps. (It appears that at some point the timestamps are interpreted at UTC without a correct timezone correction?)

Sample code:

```
In [94]: test1 = pandas.DataFrame(numpy.zeros((6,3)),index=pandas.tseries.index.date_range("2012-11-15 00:00:00", periods=6, freq="100L", tz="US/Central"))

In [95]: test2 = pandas.DataFrame(numpy.zeros((3,3)),index=pandas.tseries.index.date_range("2012-11-15 00:00:00", periods=3, freq="250L", tz="US/Central"), columns=range(3,6))

In [96]: test1
Out[96]:
                                  0  1  2
2012-11-15 00:00:00-06:00         0  0  0
2012-11-15 00:00:00.100000-06:00  0  0  0
2012-11-15 00:00:00.200000-06:00  0  0  0
2012-11-15 00:00:00.300000-06:00  0  0  0
2012-11-15 00:00:00.400000-06:00  0  0  0
2012-11-15 00:00:00.500000-06:00  0  0  0

In [97]: test2
Out[97]:
                                  3  4  5
2012-11-15 00:00:00-06:00         0  0  0
2012-11-15 00:00:00.250000-06:00  0  0  0
2012-11-15 00:00:00.500000-06:00  0  0  0

In [98]: test1.join(test2, how="outer")
Out[98]:
                                   0   1   2   3   4   5
2012-11-15 06:00:00-06:00          0   0   0   0   0   0
2012-11-15 06:00:00.100000-06:00   0   0   0 NaN NaN NaN
2012-11-15 06:00:00.200000-06:00   0   0   0 NaN NaN NaN
2012-11-15 06:00:00.250000-06:00 NaN NaN NaN   0   0   0
2012-11-15 06:00:00.300000-06:00   0   0   0 NaN NaN NaN
2012-11-15 06:00:00.400000-06:00   0   0   0 NaN NaN NaN
2012-11-15 06:00:00.500000-06:00   0   0   0   0   0   0
```
