Linear Aggregate Functions Optimization

### Current Behavior
When a query such as the following is executed:
`EXPLAIN SELECT SUM(id), SUM(id) + 1, SUM(id) + 2 FROM employees;`
The resulting execution plans are:
**Logical Plan:**
```
Projection: sum(employees.id), sum(employees.id) + Int64(1), sum(employee
s.id) + Int64(2)
Aggregate: groupBy=[[]], aggr=[[sum(CAST(employees.id AS Int64))]]
TableScan: employees projection=[id]
```
**Physical Plan:**
```
ProjectionExec: expr=[sum(employees.id)@0 as sum(employees.id), sum(emp
loyees.id)@0 + 1 as sum(employees.id) + Int64(1), sum(employees.id)@0 + 2
as sum(employees.id) + Int64(2)]
AggregateExec: mode=Single, gby=[], aggr=[sum(employees.id)]
MemoryExec: partitions=1, partition_sizes=[1]
```

As demonstrated, the repeated calculation of SUM(employees.id) is performed only
once. The subsequent additions ( +1 and +2 ) are handled by the `ProjectionExec`
through basic arithmetic operations.

### Problematic Case
Consider a more complex query inspired by Clickbench Query 29:
SELECT SUM(id), SUM(id+1), SUM(id+2), ..., SUM(id+89) FROM employees;
The execution plans are:
**Logical Plan:**
```
Aggregate: groupBy=[[]], aggr=[[sum(__common_expr_1 AS employees.id), su
m(__common_expr_1 AS employees.id + Int64(1)), ... , sum(__common_expr_1 A
S employees.id + Int64(89))]]
Projection: CAST(employees.id AS Int64) AS __common_expr_1
TableScan: employees projection=[id]
```
**Physical Plan:**
```
AggregateExec: mode=Single, gby=[], aggr=[sum(employees.id), sum(employ
ees.id + Int64(1)), ... , sum(employees.id + Int64(89))]
ProjectionExec: expr=[CAST(id@0 AS Int64) as __common_expr_1]
MemoryExec: partitions=1, partition_sizes=[1]
```

In this case, each `SUM` operation is calculated independently, which is inefficient.
The desired approach is to compute `SUM(employees.id)` once and then apply the
linearity property to compute` SUM(id + n)` as `SUM(id) + SUM(n)` for each additional
term ( 1...89 ). If the `N` is a constant (here it is) the operation should be converted as
`N*COUNT(1)` .

Other references: https://github.com/apache/datafusion/pull/15532
https://github.com/apache/datafusion/issues/15524