How to cast nested struct in Java Spark (Unsupported NullType)

I am loading mongodb to hive table and trying to solve Unsupported NullType when saveAsTable.
Sample data schema

root
 |-- level1: struct (nullable = true)
 |    |-- level2: struct (nullable = true)
 |    |    |-- level3_1: null (nullable = true)
 |    |    |-- level3_2: array (nullable = true)
 |    |    |    |-- element: struct (containsNull = true)
 |    |    |    |    |-- level4: null (nullable = true)

I tried functions.lit like

df = df.withColumn("level1.level2.level3_1", functions.lit("null").cast("string"));
       .withColumn("level1.level2.level3_2.level4", functions.lit("null").cast("string"));

but the result is like

root
 |-- level1: struct (nullable = true)
 |    |-- level2: struct (nullable = true)
 |    |    |-- level3_1: null (nullable = true)
 |    |    |-- level3_2: array (nullable = true)
 |    |    |    |-- element: struct (containsNull = true)
 |    |    |    |    |-- level4: null (nullable = true)
 |-- level1.level2.level3_1: string (nullable = false)
 |-- level1.level2.level3_2.level4: string (nullable = false)

I also checked df.na().fill() but this seems not changing the schema.

The desired result is

root
 |-- level1: struct (nullable = true)
 |    |-- level2: struct (nullable = true)
 |    |    |-- level3_1: string (nullable = true)
 |    |    |-- level3_2: array (nullable = true)
 |    |    |    |-- element: struct (containsNull = true)
 |    |    |    |    |-- level4: string (nullable = true)

and that I can use loaded mongodb data save as table to hive

Does anyone have worked on this and could give me some advise that how to cast nested nulltype or how to deal with nulltype in java. Think of a systematic/general solution that can scale for more complex data.
Many thanks

One idea is a creating schema with StringType and read data with the schema.

StructType schema = createStructType(Arrays.asList(
    createStructField("level1", createStructType(Arrays.asList(
        createStructField("level2", createStructType(Arrays.asList(
            createStructField("level3_1", StringType, true),
            createStructField("level3_2", createArrayType(createStructType(Arrays.asList(
                createStructField("level4", StringType, true)))), true)
            )), true))), true)));

// Replace new ArrayList<>() to your dataset.
Dataset<Row> df = ss.createDataFrame(new ArrayList<>(), schema);
df.printSchema();

root
 |-- level1: struct (nullable = true)
 |    |-- level2: struct (nullable = true)
 |    |    |-- level3_1: string (nullable = true)
 |    |    |-- level3_2: array (nullable = true)
 |    |    |    |-- element: struct (containsNull = true)
 |    |    |    |    |-- level4: string (nullable = true)

<br>

EDIT:

I added more intuitive example here to deliver my thought. I hope it helps you.

@Test
public void test() {
    SparkSession ss = SparkSession.builder().master("local").appName("test").getOrCreate();

    // Step1) read your mongoDB data. (I added NullType field 'level' manually for explaination.
    // https://docs.mongodb.com/spark-connector/master/python/read-from-mongodb/
    Dataset<Row> data = ss.read().json("test.json").withColumn("level", lit(null));
    data.printSchema();

    StructType schema = createStructType(Arrays.asList(
        createStructField("_id", LongType, true),
        createStructField("level", StringType, true)));

    // Step2) create newData using schema you defined. 
    Dataset<Row> newData = ss.createDataFrame(data.collectAsList(), schema);
    newData.printSchema();

    // Step3) load newData to Hive
}