To solve this optimization problem, we first need to translate the natural language description into a symbolic representation. This involves defining variables, an objective function, and constraints in terms of these variables.

Let's define:
- $x_0$ as the number of cartons of milk,
- $x_1$ as the number of packs of paper plates.

The objective function is to maximize: $9x_0 + 2x_1$

Given the information, we can translate the constraints into symbolic form as follows:

1. Total weight constraint (at least 16 pounds): $2x_0 + 5x_1 \geq 16$
2. Minimum total sustainability score: $x_0 + 11x_1 \geq 10$
3. Minimum combined value: $x_0 + x_1 \geq 8$
4. Minimum storage space requirement: $3x_0 + x_1 \geq 10$
5. Additional linear constraint: $-2x_0 + 8x_1 \geq 0$
6. Maximum total weight (no more than 49 pounds): $2x_0 + 5x_1 \leq 49$
7. Maximum total sustainability score: $x_0 + 11x_1 \leq 48$
8. Maximum combined value: $x_0 + x_1 \leq 26$
9. Maximum storage space (no more than 22 ft^2): $3x_0 + x_1 \leq 22$

The symbolic representation of the problem is thus:
```json
{
    'sym_variables': [('x0', 'cartons of milk'), ('x1', 'packs of paper plates')],
    'objective_function': '9*x0 + 2*x1',
    'constraints': [
        '2*x0 + 5*x1 >= 16',
        'x0 + 11*x1 >= 10',
        'x0 + x1 >= 8',
        '3*x0 + x1 >= 10',
        '-2*x0 + 8*x1 >= 0',
        '2*x0 + 5*x1 <= 49',
        'x0 + 11*x1 <= 48',
        'x0 + x1 <= 26',
        '3*x0 + x1 <= 22'
    ]
}
```

Now, let's implement this problem in Gurobi Python code. We will use the `gurobipy` library to model and solve this linear integer programming problem.

```python
from gurobipy import *

# Create a new model
m = Model("Optimization_Problem")

# Define variables
x0 = m.addVar(vtype=GRB.INTEGER, name="cartons_of_milk")
x1 = m.addVar(vtype=GRB.INTEGER, name="packs_of_paper_plates")

# Set the objective function
m.setObjective(9*x0 + 2*x1, GRB.MAXIMIZE)

# Add constraints
m.addConstr(2*x0 + 5*x1 >= 16, "total_weight_min")
m.addConstr(x0 + 11*x1 >= 10, "sustainability_score_min")
m.addConstr(x0 + x1 >= 8, "combined_value_min")
m.addConstr(3*x0 + x1 >= 10, "storage_space_min")
m.addConstr(-2*x0 + 8*x1 >= 0, "additional_constraint")
m.addConstr(2*x0 + 5*x1 <= 49, "total_weight_max")
m.addConstr(x0 + 11*x1 <= 48, "sustainability_score_max")
m.addConstr(x0 + x1 <= 26, "combined_value_max")
m.addConstr(3*x0 + x1 <= 22, "storage_space_max")

# Optimize the model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found.")
    print("Cartons of milk:", x0.x)
    print("Packs of paper plates:", x1.x)
    print("Objective value:", m.objVal)
else:
    print("No optimal solution found.")
```