Performance regression in combine_first()
I took another look at the testing that lead to #6479 and am still seeing ~73% slowdown. Benchmarking my application with v0.12.0 against 549a3902e2311c1c9cc3b065effe1f67d510475e found a 48x slowdown in `Series.combine_first` when using a `DatetimeIndex` with `MonthEnd` offsets. This is also present in v0.13.1, so it wasn't introduced by the fix to #6479 . The test case:

``` python
from pandas import *
rows = 2000
a = Series(np.random.random(rows), index=DatetimeIndex(start='1/1/1900', periods=rows, freq='M'))
b = Series(np.random.random(rows), index=DatetimeIndex(start='1/1/1900', periods=rows, freq='M'))

a.ix[-500:] = float('nan')

%timeit a.combine_first(b)
```

And the comparison:
![perf_head](https://f.cloud.github.com/assets/440095/2339023/80451e14-a4b0-11e3-9b4e-b746ad9a0c8f.png)

This regression does not exist for daily indices. Probably also exists for DataFrames and other offset frequencies. I'll create some vbenches later.
