Faster unstacking of dask arrays

Recent dask version support assigning to a list of ints along one dimension. we can use this for unstacking (diff builds on #5577)

diff --git i/xarray/core/variable.py w/xarray/core/variable.py
index 222e8dab9..a50dfc574 100644
--- i/xarray/core/variable.py
+++ w/xarray/core/variable.py
@@ -1593,11 +1593,9 @@ class Variable(AbstractArray, NdimSizeLenMixin, VariableArithmetic):
         else:
             dtype = self.dtype
 
-        if sparse:
+        if sparse and not is_duck_dask_array(reordered):
             # unstacking a dense multitindexed array to a sparse array
-            # Use the sparse.COO constructor until sparse supports advanced indexing
-            # https://github.com/pydata/sparse/issues/114
-            # TODO: how do we allow different sparse array types
+            # Use the sparse.COO constructor since we cannot assign to sparse.COO
             from sparse import COO
 
             codes = zip(*index.codes)
@@ -1618,19 +1616,23 @@ class Variable(AbstractArray, NdimSizeLenMixin, VariableArithmetic):
             )
 
         else:
+            # dask supports assigning to a list of ints along one axis only.
+            # So we construct an array with the last dimension flattened,
+            # assign the values, then reshape to the final shape.
+            intermediate_shape = reordered.shape[:-1] + (np.prod(new_dim_sizes),)
+            indexer = np.ravel_multi_index(index.codes, new_dim_sizes)
             data = np.full_like(
                 self.data,
                 fill_value=fill_value,
-                shape=new_shape,
+                shape=intermediate_shape,
                 dtype=dtype,
             )
 
             # Indexer is a list of lists of locations. Each list is the locations
             # on the new dimension. This is robust to the data being sparse; in that
             # case the destinations will be NaN / zero.
-            # sparse doesn't support item assigment,
-            # https://github.com/pydata/sparse/issues/114
-            data[(..., *indexer)] = reordered
+            data[(..., indexer)] = reordered
+            data = data.reshape(new_shape)
 
         return self._replace(dims=new_dims, data=data)

This should be what alignment.reindex_variables is doing but I don't fully understand that function.

The annoying bit is figuring out when to use this version and what to do with things like dask wrapping sparse. I think we want to loop over each variable in Dataset.unstack calling Variable.unstack and dispatch based on the type of Variable.data to easily handle all the edge cases.

cc @Illviljan if you're interested in implementing this

Start with Variable.unstack in xarray/core/variable.py and compare its current sparse and dask paths with alignment.reindex_variables. Trace Dataset.unstack to understand how variables are dispatched, then verify that unstacking handles dask arrays, sparse arrays, and dask-wrapped sparse data without regressing existing behavior.