Possibly reduce duplication between `signal` and `ndimage` filtering routines

Hi,

In `scipy.signal` and `scipy.ndimage`, we ship a set of
almost-if-not-completely overlapping functionality. For instance,
- `signal.order_filter` has a counterpart, `ndimage.rank_filter`;
- `signal.median_filter` can be implemented in terms of the rank filter

In fact, applying the patch below (the patch is based on the CuPy
implementation of `cupyx.signal`, see
https://github.com/cupy/cupy/blob/v11.5.0/cupyx/scipy/signal/_signaltools.py#L490),
and running `$ python dev.py test -s signal`, I only get three
failures in `medfilt`, related to
- object arrays of `Decimal` instances
- `longdouble` arrays.

So, assuming we can deprecate longdoubles and decimals in these
filters, we could potentially remove some 700 lines of obscure C code
in https://github.com/scipy/scipy/blob/main/scipy/signal/_lfilter.c.in

Thoughts?

Evgeni


P.S. The patch to check the mapping between
signal.{order,median}_filter and ndimage.rank_filter:


```diff
diff --git a/scipy/signal/_signaltools.py b/scipy/signal/_signaltools.py
index 31baba2421..2f00ead3a2 100644
--- a/scipy/signal/_signaltools.py
+++ b/scipy/signal/_signaltools.py
@@ -1492,7 +1492,15 @@ def order_filter(a, domain, rank):
         if (dimsize % 2) != 1:
             raise ValueError("Each dimension of domain argument "
                              "should have an odd number of elements.")
-    return _sigtools._order_filterND(a, domain, rank)
+
+    result = _sigtools._order_filterND(a, domain, rank)
+
+    import scipy.ndimage as ndimage
+    import numpy.testing as testing
+    res_ndi = ndimage.rank_filter(a, rank, footprint=domain, mode='constant')
+    testing.assert_equal(result, res_ndi)
+
+    return result


 def medfilt(volume, kernel_size=None):
@@ -1555,7 +1563,17 @@ def medfilt(volume, kernel_size=None):

     numels = np.prod(kernel_size, axis=0)
     order = numels // 2
-    return _sigtools._order_filterND(volume, domain, order)
+    result = _sigtools._order_filterND(volume, domain, order)
+
+
+    import scipy.ndimage as ndimage
+    import numpy.testing as testing
+    size = np.prod(kernel_size)
+    res_ndi = ndimage.rank_filter(volume, size // 2, size=kernel_size,
+                                mode='constant')
+    testing.assert_equal(result, res_ndi)
+
+    return result


 def wiener(im, mysize=None, noise=None):
```
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