[SPARK-53001][TESTS] Fix test_df_unpivot to pass with Spark 4.0

# Description

This PR proposes to fix the issue that `test_df_unpivot` doesn't  pass with Spark 4.0.
```
---- dataframe::tests::test_df_unpivot stdout ----
SparkSession Setup

thread 'dataframe::tests::test_df_unpivot' panicked at crates/connect/src/dataframe.rs:2463:9:
assertion `left == right` failed
  left: RecordBatch { schema: Schema { fields: [Field { name: "id", data_type: Int64, nullable: false, dict_id: 0, dict_is_ordered: false, metadata: {} }, Field { name: "var", data_type: Utf8, nullable: false, dict_id: 0, dict_is_ordered: false, metadata: {} }, Field { name: "val", data_type: Float32, nullable: false, dict_id: 0, dict_is_ordered: false, metadata: {} }], metadata: {} }, columns: [PrimitiveArray<Int64>
[
  1,
  1,
  2,
  2,
], StringArray
[
  "int",
  "float",
  "int",
  "float",
], PrimitiveArray<Float32>
[
  11.0,
  1.1,
  12.0,
  1.2,
]], row_count: 4 }
 right: RecordBatch { schema: Schema { fields: [Field { name: "id", data_type: Int64, nullable: false, dict_id: 0, dict_is_ordered: false, metadata: {} }, Field { name: "var", data_type: Utf8, nullable: false, dict_id: 0, dict_is_ordered: false, metadata: {} }, Field { name: "val", data_type: Float64, nullable: false, dict_id: 0, dict_is_ordered: false, metadata: {} }], metadata: {} }, columns: [PrimitiveArray<Int64>
[
  1,
  1,
  2,
  2,
], StringArray
[
  "int",
  "float",
  "int",
  "float",
], PrimitiveArray<Float64>
[
  11.0,
  1.100000023841858,
  12.0,
  1.2000000476837158,
]], row_count: 4 }
```

As of Spark 4.0, ANSI mode is enabled by default but this test doesn't consider  it.
To fix this issue, I tweaked the test so that the test pass in both ANSI mode is enabled or not.

## Related Issue(s)
SPARK-53001

Closes #4 from sarutak/fix-unpivot-test.

Authored-by: Kousuke Saruta <[email protected]>
Signed-off-by: Kousuke Saruta <[email protected]>

Author: sarutak

crates/connect/src/dataframe.rs

@@ -2445,12 +2445,14 @@ mod tests {
 
         let df = spark.create_dataframe(&data)?;
 
-        let df = df.unpivot(
-            [col("id")],
-            Some(vec![col("int"), col("float")]),
-            "var",
-            "val",
-        );
+        let df = df
+            .unpivot(
+                [col("id")],
+                Some(vec![col("int"), col("float")]),
+                "var",
+                "val",
+            )
+            .select(vec![col("id"), col("var"), col("val").cast("float")]);
 
         let res = df.collect().await?;