[Data] Increase scale of map_groups release tests from SF10 to SF100 (#58710)

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## Description
> Briefly describe what this PR accomplishes and why it's needed.

#58711 decreased the scale of the
`map_groups` tests from scale-factor 100 to scale-factor 10 because some
of the `map_groups` release tests were failing. However, after more
investigation, I realized that the only variant that doesn't work with
scale-factor 100 is the hash shuffle with autoscaling variant (see
#58734).

This PR re-increases the scale and only disables the cases that fail.

## Related issues
> Link related issues: "Fixes #1234", "Closes #1234", or "Related to
#1234".

## Additional information
> Optional: Add implementation details, API changes, usage examples,
screenshots, etc.

---------

Signed-off-by: Balaji Veeramani <bveeramani@berkeley.edu>

Author: bveeramani

release/release_data_tests.yaml

@@ -178,22 +178,32 @@
 
   matrix:
     setup:
-      # This test consistently fails on fixed-size clusters due to head OOM from
-      # too many objects references on the head node. So, we only run it on
-      # autoscaling clusters.
-      scaling: [autoscaling]
+      scaling: [fixed_size]
       shuffle_strategy: [sort_shuffle_pull_based, hash_shuffle]
       columns:
         - "column08 column13 column14"   # 84 groups
         - "column02 column14"  # 7M groups
 
+    adjustments:
+      # Ray Data OOMs with hash shuffle on autoscaling clusters. So, only run
+      # autoscaling variants with sort shuffle. For more information, see
+      # https://github.com/ray-project/ray/issues/58734.
+      - with:
+          scaling: autoscaling
+          shuffle_strategy: sort_shuffle_pull_based
+          columns: "column08 column13 column14"
+      - with:
+          scaling: autoscaling
+          shuffle_strategy: sort_shuffle_pull_based
+          columns: "column02 column14"
+
   cluster:
     cluster_compute: "{{scaling}}_all_to_all_compute.yaml"
 
   run:
     timeout: 3600
     script: >
-      python groupby_benchmark.py --sf 10 --map-groups --group-by {{columns}}
+      python groupby_benchmark.py --sf 100 --map-groups --group-by {{columns}}
       --shuffle-strategy {{shuffle_strategy}}
 
 ###############