[Feature] Support Spark expression: timestamp_add

## What is the problem the feature request solves?

> **Note:** This issue was generated with AI assistance. The specification details have been extracted from Spark documentation and may need verification.

Comet does not currently support the Spark `timestamp_add` function, causing queries using this function to fall back to Spark's JVM execution instead of running natively on DataFusion.

The `TimestampAdd` expression adds a specified quantity of time units to a timestamp value. It supports various time units (like days, hours, minutes, seconds) and is timezone-aware, returning a timestamp of the same type as the input timestamp.

Supporting this expression would allow more Spark workloads to benefit from Comet's native acceleration.

## Describe the potential solution

### Spark Specification

**Syntax:**
```sql
TIMESTAMPADD(unit, quantity, timestamp)
```

**Arguments:**
| Argument | Type | Description |
|----------|------|-------------|
| unit | String | The time unit to add (e.g., "YEAR", "MONTH", "DAY", "HOUR", "MINUTE", "SECOND") |
| quantity | Long | The number of units to add to the timestamp |
| timestamp | AnyTimestampType | The base timestamp to which the quantity will be added |
| timeZoneId | Option[String] | Optional timezone identifier for timezone-aware calculations |

**Return Type:** Returns the same data type as the input `timestamp` parameter (preserves whether it's `TimestampType` or `TimestampNTZType`).

**Supported Data Types:**
- **quantity**: `LongType` only
- **timestamp**: Any timestamp type (`TimestampType` or `TimestampNTZType`)
- **unit**: String literal representing valid time units

**Edge Cases:**
- **Null handling**: Returns null if any input parameter (quantity or timestamp) is null (`nullIntolerant = true`)
- **Timezone handling**: Automatically selects appropriate timezone based on timestamp data type
- **Unit validation**: Invalid unit strings are handled during expression conversion phase
- **Overflow**: Large quantity values may cause timestamp overflow, behavior depends on underlying `DateTimeUtils` implementation

**Examples:**
```sql
-- Add 5 days to a timestamp
SELECT TIMESTAMPADD('DAY', 5, TIMESTAMP '2010-01-01 01:02:03.123456');

-- Add 3 hours to current timestamp
SELECT TIMESTAMPADD('HOUR', 3, current_timestamp());

-- Subtract time by using negative quantity
SELECT TIMESTAMPADD('MINUTE', -30, TIMESTAMP '2010-01-01 12:00:00');
```

```scala
// DataFrame API usage
import org.apache.spark.sql.functions._

// Add 7 days to a timestamp column
df.select(expr("TIMESTAMPADD('DAY', 7, timestamp_col)"))

// Using column references for quantity
df.select(expr("TIMESTAMPADD('HOUR', quantity_col, timestamp_col)"))
```

### Implementation Approach

See the [Comet guide on adding new expressions](https://datafusion.apache.org/comet/contributor-guide/adding_a_new_expression.html) for detailed instructions.

1. **Scala Serde**: Add expression handler in `spark/src/main/scala/org/apache/comet/serde/`
2. **Register**: Add to appropriate map in `QueryPlanSerde.scala`
3. **Protobuf**: Add message type in `native/proto/src/proto/expr.proto` if needed
4. **Rust**: Implement in `native/spark-expr/src/` (check if DataFusion has built-in support first)

## Additional context

**Difficulty:** Medium
**Spark Expression Class:** `org.apache.spark.sql.catalyst.expressions.TimestampAdd`

**Related:**
- `DateAdd` - For date-only arithmetic
- `DateSub` - For subtracting from dates
- `Interval` expressions for duration-based calculations
- `TimeZoneAwareExpression` trait for timezone handling

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*This issue was auto-generated from Spark reference documentation.*