[analytics-backend-datafusion] Apply schema coercion on indexed-executor placeholder

The indexed-execution path inferred the parquet schema via build_segments
(which calls FileFormat::infer_schema) and registered it directly on the
PlaceholderProvider that from_substrait_plan binds against. The non-indexed
paths (session_context.rs, query_executor.rs, api.rs) all routed their
inferred schemas through schema_coerce::coerce_inferred_schema before
registering, but the indexed path skipped this step.

Result: an OpenSearch `ip` column lands as parquet `BinaryView` on disk;
isthmus on the Java side serializes the Substrait base schema as plain
`Binary` (Substrait has no view types). The placeholder reports `BinaryView`
while the plan declares `Binary` — DataFusion's substrait consumer rejects
the bind with:

    Substrait error: Field '<x>' in Substrait schema has a different type
    (Binary) than the corresponding field in the table schema (BinaryView).

Every analytics-engine query against an index that includes an `ip` column
(every OTEL-logs query in CalciteSearchCommandIT, for example) fails at
fragment start.

Apply coerce_inferred_schema right after build_segments, before
PlaceholderProvider construction. The placeholder, the
expr_to_bool_tree analysis, and the downstream IndexedTableProvider all
see the same Substrait-compatible (Binary / Int64 / Float32) schema, so
the bind succeeds and the parquet reader's SchemaAdapter handles the
per-batch BinaryView→Binary relabeling at scan time.

This restores parity with the other infer_schema sites; no behavior
change for non-IP columns.

Signed-off-by: Kai Huang <ahkcs@amazon.com>

Author: ahkcs

sandbox/plugins/analytics-backend-datafusion/rust/src/indexed_executor.rs

@@ -450,6 +450,7 @@ pub async unsafe fn execute_indexed_with_context(
     let (segments, schema) = build_segments(&state, Arc::clone(&store), object_metas.as_ref(), writer_generations.as_ref())
         .await
         .map_err(DataFusionError::Execution)?;
+    let schema = crate::schema_coerce::coerce_inferred_schema(schema);
     for (i, seg) in segments.iter().enumerate() {
     }