PERF: maybe_convert_numeric speedup
 xref #16043
 
Probably more to be done, but picks up some easy performance on the int path.  For some reason I'm having trouble running asv, here are some timings.

### setup
```
ints = np.array(list(range(1000000)), dtype=object)
floats = np.array([float(x) for x in range(1000000)], dtype=object)
ints_with_float = ints.copy()
ints_with_float[-1] = 1.2
```
### Master
```
In [2]: %timeit pd.to_numeric(floats)
10 loops, best of 3: 132 ms per loop

In [3]: %timeit pd.to_numeric(ints)
1 loop, best of 3: 437 ms per loop

In [4]: %timeit pd.to_numeric(ints_with_float)
1 loop, best of 3: 444 ms per loop
```

### PR
```
In [5]: %timeit pd.to_numeric(floats)
10 loops, best of 3: 88.9 ms per loop

In [6]: %timeit pd.to_numeric(ints)
10 loops, best of 3: 57.6 ms per loop

In [7]: %timeit pd.to_numeric(ints_with_float)
1 loop, best of 3: 392 ms per loop
```
