Spark: Support column default values in DDL for Iceberg V3 tables

### Feature Request / Improvement

Support Spark SQL DDL syntax for Iceberg V3 column default values.

Iceberg V3 supports column default values in table metadata, and Spark can already consume existing Iceberg defaults when writing rows with the `DEFAULT` keyword. However, Spark SQL DDL currently cannot create or evolve Iceberg column defaults.

The requested syntax includes:

```sql
CREATE TABLE local.db.t (
id INT,
data STRING DEFAULT 'default-value'
)
USING iceberg
TBLPROPERTIES ('format-version' = '3');
```

```sql
ALTER TABLE local.db.t
ADD COLUMN data STRING DEFAULT 'default-value';
```

```sql
ALTER TABLE local.db.t
ALTER COLUMN data SET DEFAULT 'new-value';
```

### Current behavior

Using the latest Iceberg `main` with Spark 4.1.2, the following operations fail:

```sql
CREATE TABLE local.db.t_create (
id INT,
data STRING DEFAULT 'default-value'
)
USING iceberg
TBLPROPERTIES ('format-version' = '3');
```

fails with:

```text
[UNSUPPORTED_FEATURE.TABLE_OPERATION] Table `local`.`db`.`t_create` does not support column default value.
```

```sql
ALTER TABLE local.db.t_alter
ADD COLUMN data STRING DEFAULT 'default-value';
```

fails with:

```text
Cannot add column data since setting default values in Spark is currently unsupported
```

```sql
ALTER TABLE local.db.t_modify
ALTER COLUMN data SET DEFAULT 'new-value';
```

fails with:

```text
Cannot apply unknown table change: org.apache.spark.sql.connector.catalog.TableChange$UpdateColumnDefaultValue
```

### Expected behavior

For Iceberg V3 tables, Spark SQL DDL should translate Spark column default table changes into Iceberg schema updates.

Expected behavior:

* `CREATE TABLE ... column DEFAULT ...` should create Iceberg columns with write defaults.
* `ALTER TABLE ... ADD COLUMN ... DEFAULT ...` should add the column with an Iceberg write default.
* `ALTER TABLE ... ALTER COLUMN ... SET DEFAULT ...` should update the Iceberg write default for the column.
* Unsupported cases, such as defaults on table format versions that do not support them, should fail with clear validation errors.

### Motivation

Column defaults are useful for schema evolution and operational compatibility. Users often manage tables through SQL rather than Java APIs. Without Spark SQL DDL support, users cannot create or update Iceberg V3 defaults from Spark even though the table
format and Iceberg API support the concept.

This creates an inconsistent experience:

* Iceberg table metadata can contain defaults.
* Spark SQL can write `DEFAULT` values when defaults already exist.
* Spark SQL cannot define or evolve those defaults through DDL.

Supporting this syntax would make Spark SQL a complete interface for managing Iceberg V3 column defaults.

### Related issue

This is related to the broader default values request:

* https://github.com/apache/iceberg/issues/10761

That issue was a general request for default values support. This issue is specifically about Spark SQL DDL support for Iceberg V3 column default metadata.

### Query engine

Spark

### Willingness to contribute

- [x] I can contribute this improvement/feature independently
- [ ] I would be willing to contribute this improvement/feature with guidance from the Iceberg community
- [ ] I cannot contribute this improvement/feature at this time