new row not created upon assignment to nonexistent row label in DataFrame with MultiIndex
According to the [documentation](http://pandas.pydata.org/pandas-docs/stable/indexing.html#setting-with-enlargement) for pandas 0.13+, assigning to a non-existent key via the .ix or .loc operations should create a new row in the DataFrame. Using pandas 0.13.1 with Python 2.7.5, I noticed that this doesn't seem to be the case when the DataFrame has a MultiIndex. To illustrate, consider a DataFrame created as follows:

```
import itertools
import numpy as np
import pandas

rows = 2 
cols = 3 
a = np.random.randint(0, 2, rows*cols).reshape((rows, cols))
b = a*np.random.rand(rows, cols)
idx = pandas.MultiIndex.from_tuples([(i, j) for i, j in \
             itertools.product(xrange(rows), xrange(cols))],
                              names=['rows','cols'])
df = pandas.DataFrame({'a': a.flatten(),
                       'b': b.flatten()}, index=idx)
```

If `df` is

```
           a         b
rows cols             
0    0     0  0.000000
     1     0  0.000000
     2     1  0.315458
1    0     0  0.000000
     1     0  0.000000
     2     1  0.061142
```

then running `df.loc[(0, 1), :] = (10, 20.0)` modifies `df` as follows:

```
            a          b
rows cols               
0    0      0   0.000000
     1     10  20.000000
     2      1   0.315458
1    0      0   0.000000
     1      0   0.000000
     2      1   0.061142
```

However, if one attempts to run `df.loc[(0,3), :] = (10, 20.0)` on the original `df`, one obtains

```
            a          b
rows cols               
0    0     10  20.000000
     1      0   0.000000
     2      1   0.315458
1    0     10  20.000000
     1      0   0.000000
     2      1   0.061142
```

Is this behavior expected?
