Set order='F' when raveling group_idx after broadcast

This majorly improves the dim=... case for engine="flox" at least.
xref #281

I'm not sure if it is a regression for engine="numpy"

We trade off a single bad reshape for array against argsorting both
array and group_idx for a ~10-20x speedup

```
ds = xr.tutorial.load_dataset('air_temperature')
ds.groupby('lon').count(..., engine="flox")
```

flox/core.py

@@ -782,16 +782,25 @@ def chunk_reduce(
     )
     groups = grps[0]
 
+    order = "C"
     if nax > 1:
         needs_broadcast = any(
             group_idx.shape[ax] != array.shape[ax] and group_idx.shape[ax] == 1
             for ax in range(-nax, 0)
         )
         if needs_broadcast:
+            # This is the dim=... case, it's a lot faster to ravel group_idx
+            # in fortran order since group_idx is then sorted
+            # I'm seeing 400ms -> 23ms for engine="flox"
+            # Of course we are slower to ravel `array` but we avoid argsorting
+            # both `array` *and* `group_idx` in _prepare_for_flox
             group_idx = np.broadcast_to(group_idx, array.shape[-by.ndim :])
+            # if engine == "flox":
+            group_idx = group_idx.reshape(-1, order="F")
+            order = "F"
     # always reshape to 1D along group dimensions
     newshape = array.shape[: array.ndim - by.ndim] + (math.prod(array.shape[-by.ndim :]),)
-    array = array.reshape(newshape)
+    array = array.reshape(newshape, order=order)
     group_idx = group_idx.reshape(-1)
 
     assert group_idx.ndim == 1