KAFKA's topic message's timestamp cannot merge with the timestamp value within the Kafka's value - apache-spark

Question

Hello everyone, the above picture is how Databricks recommend people to connect the kafka topic message to a delta live table (DLT). My current situation is I have a kafka topic that I am subcribed to, but I cannot load directly in a delta live table.
My code is as below":
#dlt.table(name = "tableOne", table_properties={"pipelines.reset.allowed":"false"})
def stream_bronze():
return(
spark
.readStream
.format("kafka")
.option("kafka.bootstrap.servers", "kafkaserver address") #this is the kafka server address
.option("subscribe","topic_name") #this is the topic name of the kafka server
.option("kafka.security.protocol", "SASL_SSL") #in order to properly "subscribe" to a kafka topic, you need to input the proper security protocals
.option("kafka.sasl.mechanism", "PLAIN") #Like above
.option("kafka.sasl.jaas.config", """kafkashaded.org.apache.kafka.common.security.plain.PlainLoginModule required username="api key" password="secret";""") #This is where you need to input the Kafka API key (username) and the Kafka API secret (password)
.load()
)
When I run the above command, I get the error that there is an issue with merging the timestamp of the kafka message and the timeStamp of the kafka message value (so the content inside the kafka message. It goes by the format of a json).
The kafka schema that is fixed is like this:
And the schema of the details inside value looks like this:
schema = StructType([
StructField("event", StringType()),
StructField("ecommerce" , StringType()),
StructField("gtm.uniqueEventId" , StringType()),
StructField("eventId" , StringType()),
StructField("eventType" , StringType()),
StructField("pagePath" , StringType()),
StructField("pageType" , StringType()),
StructField("isRegistered" , StringType()),
StructField("memberId" , StringType()),
StructField("sessionId" , StringType()),
StructField("campaignId" , StringType()),
StructField("elabRecommType" , StringType()),
StructField("elabRecommId" , StringType()),
StructField("gaUserId" , StringType()),
StructField("gaSessionId" , StringType()),
StructField("siteCode" , StringType()),
StructField("timeStamp" , StringType()),
])
The issue that I get is that timestamp from the kafka and timeStamp from the schema of the json inside of the kafka message's value is different. One is TimestampType and the second one is StringType.
The error message that I get is:
org.apache.spark.sql.AnalysisException: Failed to merge fields 'timeStamp' and 'timestamp'. Failed to merge incompatible data types StringType and TimestampType
Of course, if I could modify the type directly from the kafka message producer it would be great but I do not have that kind of permission. Is there a way for me to make it possible fr the streaming of the kafka message successful on a delta live table?
Any help is appreciated. Thanks!
Edit:
I run the following code to apply the schema, but this is not really what is preventing me. even with this bit out. The action of simply asking the dlt to return the kafka readStream is not working. The error being the one mentioned above
wtchk_value_df = wtchk_kafka_df.select(from_json(col("value").cast("string"), schema).alias("value"))