034: fix double JSON serialization in AsyncAPIClient.request

Root cause: AsyncAPIClient.request() passes its  parameter directly
to httpx.AsyncClient.request(json=data). When callers pass a pre-serialized
JSON string (data=json.dumps(dict)), httpx re-encodes it via json.dumps(),
resulting in a double-encoded JSON string body instead of a JSON object.

This caused ALL POST/PUT requests with string data to fail — Superset received
a JSON string instead of a JSON object, returning GENERIC_BACKEND_ERROR
('dictionary update sequence element #0 has length 1; 2 is required').

Fix: if data is a string, pass it via httpx  parameter (raw body);
if it's a dict/list, pass via  for automatic encoding.

Affected callers (6 files) now correctly send JSON objects:
  - preview_executor.py: chart data requests
  - superset_executor.py
  - _run_source.py
  - _datasets.py: update_dataset
  - _datasets_preview.py: compile_dataset_preview
  - _dashboards_write.py

Also simplified preview_executor.fetch_sample_rows back to single-strategy
(chart data API only) since the root cause is now fixed.

backend/src/plugins/translate/preview_executor.py

@@ -37,7 +37,7 @@ class PreviewExecutor:
 
     # region fetch_sample_rows [TYPE Function]
     # @PURPOSE: Fetch sample rows from Superset dataset for preview.
-    # @SIDE_EFFECT  Calls Superset chart data endpoint.
+    # @SIDE_EFFECT  Calls Superset chart data API.
     async def fetch_sample_rows(
         self,
         job: TranslationJob,
@@ -60,13 +60,20 @@ class PreviewExecutor:
                 dataset_id=int(job.source_datasource_id), dataset_record=dataset_detail,
                 template_params={}, effective_filters=[],
             )
+
+            # Build column list from dataset schema for explicit column projection
+            column_names = [
+                col["name"] for col in dataset_detail.get("columns", [])
+                if col.get("name") and col.get("is_active", True)
+            ]
+
             queries = query_context.get("queries", [])
             if queries:
                 queries[0]["row_limit"] = sample_size
-                queries[0].pop("result_type", None)
-                queries[0].pop("columns", None)
+                queries[0]["columns"] = column_names or []
                 queries[0]["metrics"] = []
-            query_context["result_type"] = "samples"
+                queries[0].pop("result_type", None)
+            query_context["result_type"] = "query"
             form_data = query_context.get("form_data", {})
             form_data.pop("query_mode", None)