[Spark] Honor codegen configs in DataSkippingStatsTracker (#4120)

#### Which Delta project/connector is this regarding?

- [x] Spark
- [ ] Standalone
- [ ] Flink
- [ ] Kernel
- [ ] Other (fill in here)

## Description

This PR uses MutableProjection.create instead of directly generating the
MutableProjection via GeneratMutableProjection.generate. As the latter
forces codegen, it can fail if the generated code is too large, which,
e.g., can happen for very wide schemas. The new code uses the correct
Spark API to automatically fall back to an InterpretedMutableProjection
if codegen fails for any reason.

## How was this patch tested?

This PR just uses the correct, higher-level API call to create a
MutableProjection so existing tests are sufficient.

## Does this PR introduce _any_ user-facing changes?

No

spark/src/main/scala/org/apache/spark/sql/delta/sources/DeltaSQLConf.scala

@@ -2273,6 +2273,16 @@ trait DeltaSQLConfBase {
       .checkValue(v => v >= 1, "Must be at least 1.")
       .createWithDefault(100)
 
+  val DELTA_STATS_COLLECTION_FALLBACK_TO_INTERPRETED_PROJECTION =
+    buildConf("collectStats.fallbackToInterpretedProjection")
+      .internal()
+      .doc("When enabled, the updateStats expression will use the standard code path" +
+        " that falls back to an interpreted expression if codegen fails. This should" +
+        " always be true. The config only exists to force the old behavior, which was" +
+        " to always use codegen.")
+      .booleanConf
+      .createWithDefault(true)
+
   val DELTA_CONVERT_ICEBERG_STATS = buildConf("collectStats.convertIceberg")
     .internal()
     .doc("When enabled, attempts to convert Iceberg stats to Delta stats when cloning from " +

spark/src/main/scala/org/apache/spark/sql/delta/stats/DataSkippingStatsTracker.scala

@@ -19,6 +19,7 @@ package org.apache.spark.sql.delta.stats
 import scala.collection.mutable
 
 import org.apache.spark.sql.delta.expressions.JoinedProjection
+import org.apache.spark.sql.delta.sources.DeltaSQLConf
 import org.apache.hadoop.conf.Configuration
 import org.apache.hadoop.fs.{FileSystem, Path}
 
@@ -27,6 +28,7 @@ import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.expressions.aggregate._
 import org.apache.spark.sql.catalyst.expressions.codegen.GenerateMutableProjection
 import org.apache.spark.sql.execution.datasources._
+import org.apache.spark.sql.internal.SQLConf
 import org.apache.spark.sql.types._
 import org.apache.spark.util.SerializableConfiguration
 
@@ -87,14 +89,23 @@ class DeltaTaskStatisticsTracker(
 
   // This projection combines the intermediate results stored by aggBuffer with the values of the
   // currently processed row and updates aggBuffer in place.
-  private val updateStats: MutableProjection = GenerateMutableProjection.generate(
-    expressions = JoinedProjection.bind(
-      aggBufferAttrs,
-      dataCols,
-      aggregates.flatMap(_.updateExpressions)),
-    inputSchema = Nil,
-    useSubexprElimination = true
-  )
+  private val updateStats: MutableProjection = {
+    val aggs = aggregates.flatMap(_.updateExpressions)
+    val expressions = JoinedProjection.bind(aggBufferAttrs, dataCols, aggs)
+    if (SQLConf.get.getConf(
+        DeltaSQLConf.DELTA_STATS_COLLECTION_FALLBACK_TO_INTERPRETED_PROJECTION)) {
+      MutableProjection.create(
+        exprs = expressions,
+        inputSchema = Nil
+      )
+    } else {
+      GenerateMutableProjection.generate(
+        expressions = expressions,
+        inputSchema = Nil,
+        useSubexprElimination = true
+      )
+    }
+  }
 
   // This executes the whole statsColExpr in order to compute the final stats value for the file.
   // In order to evaluate it, we have to replace its aggregate functions with the corresponding