Only pass batches to retry for microbatch model when there was a Part…

…ialSuccess

In the previous commit we made it so that retries of microbatch models wouldn't
run in full refresh mode when the microbatch model to retry has batches already
specified from the prior run. This is only problematic when the run being retried
was a full refresh AND all the batches for a given microbatch model failed. In
that case WE DO want to do a full refresh for the given microbatch model. To better
outline the problem, consider the following:

* a microbatch model had a begin of `2020-01-01` and has been running this way for awhile
* the begin config has changed to `2024-01-01` and  dbt run --full-refresh gets run
* every batch for an microbatch model fails
* on dbt retry the the relation is said to exist, and the now out of range data (2020-01-01 through 2023-12-31) is never purged

To avoid this, all we have to do is ONLY pass the batch information for partially successful microbatch
models. Note: microbatch models only have a partially successful status IFF they have both
successful and failed batches.

core/dbt/task/retry.py

@@ -132,7 +132,9 @@ def run(self):
         batch_map = {
             result.unique_id: result.batch_results.failed
             for result in self.previous_results.results
-            if result.batch_results is not None and len(result.batch_results.failed) > 0
+            if result.status == NodeStatus.PartialSuccess
+            and result.batch_results is not None
+            and len(result.batch_results.failed) > 0
         }
 
         class TaskWrapper(self.task_class):