feat(data types): implement data types

[sjrusso8]

Description

Implement data types based on the Spark Data Types

• Create additional trait for CastToDataType to allow for a user to leverage DataType or &str when using col("name").cast(...)
• Added the schema method on the DataFrameReader to specify the read schema
• Create additional trait for ToSchema to allow users to specific a read schema using a StructType schema or &str representing a DDL string.
• Updated the reader example to show a type cast with a DataType
• Removed ToProtoType and implemented From<T> for spark::DataType instead. It's more idiomatic
• Misc: reorganized the code in the column module and changed up some variable names

Not Implemented

• The fromJson method that StructType and StructField have on the current API.
• The dataframe method for schema() still returns the types from the protobuf. Will implement this change later.

Usage

As a schema option

let schema = StructType::new(vec![
    StructField {
        name: "name",
        data_type: DataType::String,
        nullable: false,
        metadata: None,
    },
    StructField {
        name: "age",
        data_type: DataType::Short,
        nullable: true,
        metadata: None,
    },
]);

let df = spark
    .clone()
    .read()
    .format("json")
    .schema(schema)
    .load(path)?;

As a column cast

let df = df
    .select([
        F::col("name").cast(DataType::String).alias("name_str"),
        F::col("age").cast(DataType::Integer).alias("age_int"),
    ]);