[MINOR][ML][DOCS] Fix sql data types link in the ml-pipeline page

### What changes were proposed in this pull request?

<img width="939" alt="image" src="https://user-images.githubusercontent.com/8326978/169767919-6c48554c-87ff-4d40-a47d-ec4da0c993f7.png">

[Spark SQL datatype reference](https://spark.apache.org/docs/latest/sql-reference.html#data-types) - `https://spark.apache.org/docs/latest/sql-reference.html#data-types` is invalid and it shall be [Spark SQL datatype reference](https://spark.apache.org/docs/latest/sql-ref-datatypes.html) - `https://spark.apache.org/docs/latest/sql-ref-datatypes.html`
https://spark.apache.org/docs/latest/ml-pipeline.html#dataframe

### Why are the changes needed?

doc fix

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

no

### How was this patch tested?

`bundle exec jekyll serve`

Closes #36633 from yaooqinn/minor.

Authored-by: Kent Yao <yao@apache.org>
Signed-off-by: huaxingao <huaxin_gao@apple.com>

docs/ml-pipeline.md

@@ -72,7 +72,7 @@ E.g., a learning algorithm is an `Estimator` which trains on a `DataFrame` and p
 Machine learning can be applied to a wide variety of data types, such as vectors, text, images, and structured data.
 This API adopts the `DataFrame` from Spark SQL in order to support a variety of data types.
 
-`DataFrame` supports many basic and structured types; see the [Spark SQL datatype reference](sql-reference.html#data-types) for a list of supported types.
+`DataFrame` supports many basic and structured types; see the [Spark SQL datatype reference](sql-ref-datatypes.html) for a list of supported types.
 In addition to the types listed in the Spark SQL guide, `DataFrame` can use ML [`Vector`](mllib-data-types.html#local-vector) types.
 
 A `DataFrame` can be created either implicitly or explicitly from a regular `RDD`.  See the code examples below and the [Spark SQL programming guide](sql-programming-guide.html) for examples.