DOC:/USAGE: Resulting Index when merging over indices
#### Location of the documentation

https://dev.pandas.io/docs/reference/api/pandas.DataFrame.merge.html

#### Documentation problem

There is no documentation on this case

#### Suggested fix for documentation

I'm a bit confused about the behavior here, because I don't know what to expect. When I perform the merge as shown below, I would expect that both operations return the same output.

```
left = pd.DataFrame({'a': [1, 2], 'b': [1, 1], "l": [22, 23]}).set_index(['a', 'b'])
right = pd.DataFrame({'b': [1], "r": [12]}).set_index(['b'])
print(pd.merge(left, right, left_on=['b'], right_index=True, how="left"))
print(pd.merge(left, right, left_on=['b'], right_on=["b"], how="left"))
```

But the Index of both results differs. The first merge has the same index as the left `DataFrame` while the second merge has only `b` as index. As a result, the index from the second merge is no longer unique.

**First Merge:**
```
      l   r
a b        
1 1  22  12
2 1  23  12
```

**Second Merge:**

```
    l   r
b        
1  22  12
1  23  12
```

**If** this is the desired behavior, we should adjust the documentation to show this. If it is not, I would file an BUG issue. We should adjust the documentation nevertheless. I would add an example with an index join to show the right behavior.
