## Step 1: Define the symbolic representation of the variables
The variables are 'cartons of milk' and 'packs of paper plates', which we can denote as $x_1$ and $x_2$ respectively.

## Step 2: Translate the objective function into symbolic notation
The objective function to maximize is $9x_1 + 2x_2$.

## Step 3: List the constraints in symbolic notation
The constraints given are:
1. $2x_1 + 5x_2 \geq 16$ (total weight at least 16 pounds)
2. $x_1 + 11x_2 \geq 10$ (total sustainability score at least 10)
3. $x_1 + x_2 \geq 8$ (total value at least $8)
4. $3x_1 + x_2 \geq 10$ (total storage space at least 10 ft^2)
5. $-2x_1 + 8x_2 \geq 0$ (linear constraint)
6. $2x_1 + 5x_2 \leq 49$ (total weight at most 49 pounds)
7. $x_1 + 11x_2 \leq 48$ (total sustainability score at most 48)
8. $x_1 + x_2 \leq 26$ (total value at most $26)
9. $3x_1 + x_2 \leq 22$ (total storage space at most 22 ft^2)
10. $x_1, x_2 \geq 0$ and are integers.

## 4: Create the symbolic representation of the problem
```json
{
'sym_variables': [('x1', 'cartons of milk'), ('x2', 'packs of paper plates')],
'objective_function': '9*x1 + 2*x2',
'constraints': [
    '2*x1 + 5*x2 >= 16',
    'x1 + 11*x2 >= 10',
    'x1 + x2 >= 8',
    '3*x1 + x2 >= 10',
    '-2*x1 + 8*x2 >= 0',
    '2*x1 + 5*x2 <= 49',
    'x1 + 11*x2 <= 48',
    'x1 + x2 <= 26',
    '3*x1 + x2 <= 22',
    'x1 >= 0',
    'x2 >= 0'
]
}
```

## Step 5: Write the Gurobi code to solve the optimization problem
```python
import gurobi

def solve_optimization_problem():
    # Create a new Gurobi model
    model = gurobi.Model()

    # Define the variables
    x1 = model.addVar(name="cartons_of_milk", vtype=gurobi.GRB.INTEGER)
    x2 = model.addVar(name="packs_of_paper_plates", vtype=gurobi.GRB.INTEGER)

    # Define the objective function
    model.setObjective(9 * x1 + 2 * x2, gurobi.GRB.MAXIMIZE)

    # Add constraints
    model.addConstr(2 * x1 + 5 * x2 >= 16, name="weight_constraint")
    model.addConstr(x1 + 11 * x2 >= 10, name="sustainability_score_constraint")
    model.addConstr(x1 + x2 >= 8, name="value_constraint")
    model.addConstr(3 * x1 + x2 >= 10, name="storage_space_constraint")
    model.addConstr(-2 * x1 + 8 * x2 >= 0, name="linear_constraint")
    model.addConstr(2 * x1 + 5 * x2 <= 49, name="max_weight_constraint")
    model.addConstr(x1 + 11 * x2 <= 48, name="max_sustainability_score_constraint")
    model.addConstr(x1 + x2 <= 26, name="max_value_constraint")
    model.addConstr(3 * x1 + x2 <= 22, name="max_storage_space_constraint")

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Cartons of milk: {x1.varValue}")
        print(f"Packs of paper plates: {x2.varValue}")
        print(f"Objective function value: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```