PERF: perf regression with mixed-type ops using numexpr (GH5481)
closes #5481

BUG: non-unique ops not aligning correctly

these are bascially a trivial op in numpy, so numexpr is slightly slower (but the
the dtype inference issue is fixed). Essentially the recreation of an int64 ndarray had to check if its a datetime-like. In this case just passing in the dtype on the reconstructed series fixes it.

Also handles non-unique columns now (no tests before, and it would fail).

```
In [1]: df = pd.DataFrame({"A": np.arange(1000000), "B": np.arange(1000000, 0, -1), "C": np.random.randn(1000000)})

In [2]: pd.computation.expressions.set_use_numexpr(False)

In [3]: %timeit df*df
100 loops, best of 3: 11 ms per loop

In [4]: pd.computation.expressions.set_use_numexpr(True)

In [5]: %timeit df*df
100 loops, best of 3: 15.7 ms per loop

In [6]: df = df.astype(float)

In [7]: pd.computation.expressions.set_use_numexpr(False)

In [8]: %timeit df*df
100 loops, best of 3: 5.16 ms per loop

In [9]: pd.computation.expressions.set_use_numexpr(True)

In [10]: %timeit df*df
100 loops, best of 3: 5.37 ms per loop

```
