## Step 1: Define the symbolic representation of the variables
The variables are 'packs of napkins', 'candles', and 'lightbulbs', which we can denote as $x_0$, $x_1$, and $x_2$ respectively.

## Step 2: Convert the natural language objects into symbolic notation
The symbolic representation of the variables is:
- $x_0$ : packs of napkins
- $x_1$ : candles
- $x_2$ : lightbulbs

## 3: Define the objective function in symbolic notation
The objective function to minimize is $8x_0 + 7x_1 + 3x_2$.

## 4: List the constraints in symbolic notation
The constraints are:
1. $8x_0 + 8x_1 + 3x_2 \geq 17$ (total combined usefulness rating from all items)
2. $8x_0 + 8x_1 \geq 19$ (total combined usefulness rating from packs of napkins and candles)
3. $8x_1 + 3x_2 \geq 17$ (total combined usefulness rating from candles and lightbulbs)
4. $9x_0 + 2x_1 + 10x_2 \geq 15$ (total combined portability rating from all items)
5. $9x_0 + 2x_1 \geq 0$ is not a constraint, instead $2x_1 + 10x_2 \geq 17$ (total combined portability rating from candles and lightbulbs)
6. $2x_0 + 5x_1 + 6x_2 \geq 22$ (total combined storage space from all items)
7. $2x_0 + 5x_1 \geq 11$ (storage space from packs of napkins and candles)
8. $2x_0 + 6x_2 \geq 8$ (storage space from packs of napkins and lightbulbs)
9. $-7x_0 + 8x_2 \geq 0$ 
10. $-9x_1 + 9x_2 \geq 0$
11. $2x_1 + 10x_2 \leq 75$ (total combined portability rating from candles and lightbulbs)
12. $9x_0 + 2x_1 \leq 60$ (total combined portability rating from packs of napkins and candles)
13. $2x_0 + 5x_1 + 6x_2 \leq 42$ (total storage space)

## 5: Define the symbolic representation of the problem
```json
{
    'sym_variables': [('x0', 'packs of napkins'), ('x1', 'candles'), ('x2', 'lightbulbs')],
    'objective_function': '8*x0 + 7*x1 + 3*x2',
    'constraints': [
        '8*x0 + 8*x1 + 3*x2 >= 17',
        '8*x0 + 8*x1 >= 19',
        '8*x1 + 3*x2 >= 17',
        '9*x0 + 2*x1 + 10*x2 >= 15',
        '2*x1 + 10*x2 >= 17',
        '2*x0 + 5*x1 + 6*x2 >= 22',
        '2*x0 + 5*x1 >= 11',
        '2*x0 + 6*x2 >= 8',
        '-7*x0 + 8*x2 >= 0',
        '-9*x1 + 9*x2 >= 0',
        '2*x1 + 10*x2 <= 75',
        '9*x0 + 2*x1 <= 60',
        '2*x0 + 5*x1 + 6*x2 <= 42',
        'x0 >= 0 and x0 % 1 == 0',
        'x1 >= 0 and x1 % 1 == 0',
        'x2 >= 0 and x2 % 1 == 0'
    ]
}
```

## Step 6: Write the Gurobi code
```python
import gurobi

def solve_optimization_problem():
    model = gurobi.Model()

    # Define variables
    x0 = model.addVar(name="x0", vtype=gurobi.GRB.INTEGER)  # packs of napkins
    x1 = model.addVar(name="x1", vtype=gurobi.GRB.INTEGER)  # candles
    x2 = model.addVar(name="x2", vtype=gurobi.GRB.INTEGER)  # lightbulbs

    # Objective function
    model.setObjective(8 * x0 + 7 * x1 + 3 * x2, gurobi.GRB.MINIMIZE)

    # Constraints
    model.addConstr(8 * x0 + 8 * x1 + 3 * x2 >= 17)
    model.addConstr(8 * x0 + 8 * x1 >= 19)
    model.addConstr(8 * x1 + 3 * x2 >= 17)
    model.addConstr(9 * x0 + 2 * x1 + 10 * x2 >= 15)
    model.addConstr(2 * x1 + 10 * x2 >= 17)
    model.addConstr(2 * x0 + 5 * x1 + 6 * x2 >= 22)
    model.addConstr(2 * x0 + 5 * x1 >= 11)
    model.addConstr(2 * x0 + 6 * x2 >= 8)
    model.addConstr(-7 * x0 + 8 * x2 >= 0)
    model.addConstr(-9 * x1 + 9 * x2 >= 0)
    model.addConstr(2 * x1 + 10 * x2 <= 75)
    model.addConstr(9 * x0 + 2 * x1 <= 60)
    model.addConstr(2 * x0 + 5 * x1 + 6 * x2 <= 42)

    # Solve the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Objective: ", model.objVal)
        print("x0: ", x0.varValue)
        print("x1: ", x1.varValue)
        print("x2: ", x2.varValue)
    else:
        print("The model is infeasible")

solve_optimization_problem()
```