MCP `generate_chart` returns 'Empty query?' for virtual datasets that use Jinja templating in their SQL

### Bug description

The `superset.mcp_service` `generate_chart` tool (and the chart-preview code path it invokes) does not render Jinja in the underlying virtual dataset's SQL. Any chart generation against a virtual dataset that depends on a Jinja variable fails with `error_type: compile_error`, `details: "Empty query?"`, and `error_code: CHART_COMPILE_FAILED`. The chart cannot be saved via MCP.

### Environment

- Apache Superset image: `apachesuperset.docker.scarf.sh/apache/superset:6.1.0rc1`
- MCP server: `superset.mcp_service` running via `fastmcp` `streamable-http` transport (in-process with Flask app), started from the same image as a sidecar container.
- ClickHouse backend (also reproducible on other engines — the SQL is rendered unrendered before reaching the engine, so engine choice is incidental).
- `ENABLE_TEMPLATE_PROCESSING` is enabled (Jinja works fine via the regular Explore UI and via SQL Lab).

### How to reproduce

1. Create a virtual dataset whose SQL references a Jinja variable, e.g.:
```sql
SELECT *
FROM gold_layer.member_enriched_v1
WHERE email = '{{ url_param("member_email", "default@example.com") }}'
```
(Also reproducible with bare `{{ member_email }}` plus a dataset-level Template Parameters default, and with the Jinja `default` filter.)
2. Call the MCP `generate_chart` tool against that dataset, e.g.:
```json
{
"dataset_id": ,
"save_chart": false,
"config": {
"chart_type": "table",
"columns": [{"name": "some_column"}]
}
}
```
3. Observe the error response:
```json
{
"success": false,
"error": {
"error_type": "compile_error",
"message": "Chart query failed to execute. The chart configuration is invalid.",
"details": "Error: Empty query?",
"error_code": "CHART_COMPILE_FAILED"
}
}
```

### Expected behavior

The chart-preview pipeline should render the dataset's Jinja (using `url_param` defaults, dataset-level Template Parameters, or both) the same way the regular Explore UI does. The preview query should execute and the chart should save.

### Actual behavior

The Jinja templating in the dataset SQL is not invoked at all in the MCP chart-generation code path. Confirmed by inspecting `executed_sql` returned from `execute_sql` when no `template_params` argument is passed — the Jinja literal `{{ ... }}` is sent through to the engine unrendered, despite the dataset having Template Parameters configured.

### Additional observations

- `execute_sql` only renders Jinja when `template_params` is passed explicitly as a tool argument. The dataset's saved Template Parameters are not consulted by either `execute_sql` or `generate_chart`.
- Setting `template_params` on the dataset via the dataset edit modal persists the value (verified through `list_datasets` `template_params` field), but it has no effect on chart generation via MCP.
- The same dataset works correctly in the Superset UI: opening Explore, building a chart, and saving from the UI all render Jinja properly.
- Workaround: create the chart in the Superset UI. There is no MCP-only workaround for this dataset shape.

### Impact

Any team relying on virtual datasets parameterized with Jinja (a standard Superset pattern for high-cardinality lookups, multi-tenant filtering, etc.) cannot use MCP to build charts/dashboards against those datasets. In our case the underlying table has 400M+ rows, so Jinja-based parameterization is required — the MCP is effectively unusable for this dataset.

### Suggested fix

The MCP chart-preview path should invoke the same `BaseTemplateProcessor` / `get_template_processor()` flow that the regular Explore endpoint uses when materializing the virtual-dataset SQL, including reading the dataset's `template_params` and any `url_param` defaults.