## Step 1: Define the symbolic representation of the variables
The variables are 'packs of napkins', 'diapers', and 'candles', which we will denote as $x_1$, $x_2$, and $x_3$ respectively.

## Step 2: Convert the objective function into symbolic notation
The objective function to minimize is $2 \times (\text{packs of napkins}) + 6 \times (\text{diapers}) + 7 \times (\text{candles})$, which in symbolic notation is $2x_1 + 6x_2 + 7x_3$.

## 3: List the constraints in symbolic notation
1. $\text{packs of napkins weigh } 12 \text{ lbs}$: $12x_1$ (no constraint on $x_1$ directly, but a bound)
2. $\text{packs of napkins are } 6 \text{ dollars each}$: $6x_1$ (similarly, no direct constraint)
3. $\text{packs of napkins have a usefulness rating of } 14 \text{ each}$: $14x_1$
4. $\text{packs of napkins have a sustainability score of } 14 \text{ each}$: $14x_1$
5. $\text{diapers are } 11 \text{ pounds in weight}$: $11x_2$
6. $\text{diapers are } \$9$: $9x_2$
7. $\text{diapers each have a usefulness rating of } 3$: $3x_2$
8. $\text{diapers each have a sustainability score of } 6$: $6x_2$
9. $\text{candles each weigh } 11 \text{ pounds}$: $11x_3$
10. $\text{candles cost } 5 \text{ dollars each}$: $5x_3$
11. $\text{candles have a usefulness rating of } 1$: $x_3$
12. $\text{The sustainability score of candles is } 9$: $9x_3$
13. $\text{The total weight of packs of napkins plus diapers should be at least } 11 \text{ pounds}$: $12x_1 + 11x_2 \geq 11$
14. $\text{The total weight of packs of napkins, diapers, and candles must be at least } 34 \text{ lbs}$: $12x_1 + 11x_2 + 11x_3 \geq 34$
15. $\text{You must spend at least } \$42 \text{ on diapers plus candles}$: $9x_2 + 5x_3 \geq 42$
16. $\text{You have to spend at least } \$43 \text{ on packs of napkins, and diapers}$: $6x_1 + 9x_2 \geq 43$
17. $\text{You have to spend at least } \$27 \text{ on packs of napkins, diapers, and candles}$: $6x_1 + 9x_2 + 5x_3 \geq 27$
18. $\text{The total combined usefulness rating from packs of napkins plus diapers has to be as much or more than } 26$: $14x_1 + 3x_2 \geq 26$
19. $\text{The total combined usefulness rating from packs of napkins, diapers, and candles has to be } 26 \text{ at minimum}$: $14x_1 + 3x_2 + x_3 \geq 26$
20. $\text{The total combined sustainability score from diapers plus candles must be at minimum } 45$: $6x_2 + 9x_3 \geq 45$
21. $\text{The total combined sustainability score from packs of napkins, and diapers must be } 22 \text{ at a minimum}$: $14x_1 + 6x_2 \geq 22$
22. $\text{The total combined sustainability score from packs of napkins plus diapers plus candles has to be as much or more than } 33$: $14x_1 + 6x_2 + 9x_3 \geq 33$
23. $-1 \times (\text{packs of napkins}) + 7 \times (\text{candles}) \geq 0$: $-x_1 + 7x_3 \geq 0$
24. $6 \times (\text{packs of napkins}) + -2 \times (\text{diapers}) \geq 0$: $6x_1 - 2x_2 \geq 0$
25. $\text{You must spend no more than } 117 \text{ dollars on packs of napkins plus diapers plus candles}$: $6x_1 + 9x_2 + 5x_3 \leq 117$
26. $x_1, x_2, x_3$ are integers.

## 4: Provide the symbolic representation of the problem
```json
{
    'sym_variables': [
        ('x1', 'packs of napkins'),
        ('x2', 'diapers'),
        ('x3', 'candles')
    ],
    'objective_function': '2*x1 + 6*x2 + 7*x3',
    'constraints': [
        '12*x1 + 11*x2 >= 11',
        '12*x1 + 11*x2 + 11*x3 >= 34',
        '9*x2 + 5*x3 >= 42',
        '6*x1 + 9*x2 >= 43',
        '6*x1 + 9*x2 + 5*x3 >= 27',
        '14*x1 + 3*x2 >= 26',
        '14*x1 + 3*x2 + x3 >= 26',
        '6*x2 + 9*x3 >= 45',
        '14*x1 + 6*x2 >= 22',
        '14*x1 + 6*x2 + 9*x3 >= 33',
        '-x1 + 7*x3 >= 0',
        '6*x1 - 2*x2 >= 0',
        '6*x1 + 9*x2 + 5*x3 <= 117'
    ]
}
```

## 5: Write the Gurobi code
```python
import gurobi

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

    # Define the variables
    x1 = model.addVar(name="x1", vtype=gurobi.GRB.INTEGER)  # packs of napkins
    x2 = model.addVar(name="x2", vtype=gurobi.GRB.INTEGER)  # diapers
    x3 = model.addVar(name="x3", vtype=gurobi.GRB.INTEGER)  # candles

    # Define the objective function
    model.setObjective(2 * x1 + 6 * x2 + 7 * x3, gurobi.GRB.MINIMIZE)

    # Add constraints
    model.addConstr(12 * x1 + 11 * x2 >= 11)
    model.addConstr(12 * x1 + 11 * x2 + 11 * x3 >= 34)
    model.addConstr(9 * x2 + 5 * x3 >= 42)
    model.addConstr(6 * x1 + 9 * x2 >= 43)
    model.addConstr(6 * x1 + 9 * x2 + 5 * x3 >= 27)
    model.addConstr(14 * x1 + 3 * x2 >= 26)
    model.addConstr(14 * x1 + 3 * x2 + x3 >= 26)
    model.addConstr(6 * x2 + 9 * x3 >= 45)
    model.addConstr(14 * x1 + 6 * x2 >= 22)
    model.addConstr(14 * x1 + 6 * x2 + 9 * x3 >= 33)
    model.addConstr(-x1 + 7 * x3 >= 0)
    model.addConstr(6 * x1 - 2 * x2 >= 0)
    model.addConstr(6 * x1 + 9 * x2 + 5 * x3 <= 117)

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Objective: ", model.objVal)
        print("x1: ", x1.varValue)
        print("x2: ", x2.varValue)
        print("x3: ", x3.varValue)
    else:
        print("The model is infeasible")

solve_optimization_problem()
```