[BUG] `mx.distributed.all_max` / `all_min` silently drop `NaN` (inconsistent with `mx.max`/`mx.min`)

Describe the bug

The distributed cross-rank reduce uses std::max / std::min
(mlx/distributed/reduction_ops.h:17-35), which are not NaN-propagating
(std::max(nan, x) returns x when nan is the first argument). So if
exactly one rank has a NaN at a position, all_max / all_min return
the other ranks' non-NaN value and the NaN vanishes — the failure is
silently swallowed.

To Reproduce

import mlx.core as mx
# 2-rank group
nan = float("nan")
x = mx.array([1.0, nan if rank == 0 else 2.0, 3.0])  # rank 0 has NaN at idx 1
m = mx.distributed.all_max(x)
mx.eval(m)   # -> array([1.0, 2.0, 3.0])   WRONG: should be [1.0, nan, 3.0]

Expected behavior

[rank 0] AMAX [1.0, 2.0, 3.0]
[rank 0] AMIN [1.0, 0.5, 3.0]
[rank 1] AMAX [1.0, 2.0, 3.0]
[rank 1] AMIN [1.0, 0.5, 3.0]
local mx.max([1,nan,3]) = nan (propagates NaN)
local mx.min([1,nan,3]) = nan (propagates NaN)

BUG REPRODUCED: all_max dropped the single-rank NaN (mx.max would propagate it).

Read mlx/distributed/reduction_ops.h:17-35 and run the two-rank Python reproducer. Done when mx.distributed.all_max and all_min preserve a NaN at a position held by exactly one rank, matching mx.max and mx.min propagation on every rank.