## Step 1: Define the symbolic representation of the variables
The variables are 'candles', 'toilet paper rolls', and 'diapers', which can be represented symbolically as $x_0$, $x_1$, and $x_2$ respectively.

## Step 2: Convert the natural language description into a symbolic representation
The symbolic representation of the variables is:
- $x_0$ : candles
- $x_1$ : toilet paper rolls
- $x_2$ : diapers

## Step 3: Define the objective function in symbolic notation
The objective function to minimize is $1x_0 + 4x_1 + 7x_2$.

## 4: List the constraints in symbolic notation
The constraints are:
- $27x_0 \geq 73 - 22x_2$ (total combined usefulness rating from candles and diapers must be as much or more than 73)
- $27x_0 + 24x_1 + 22x_2 \geq 73$ (total combined usefulness rating from candles, toilet paper rolls, and diapers must be as much or more than 73)
- $9x_0 + 13x_1 \geq 132$ (must use at least 132 square feet of storage space on candles plus toilet paper rolls)
- $9x_0 + 31x_2 \geq 138$ (must take up at least 138 sq. ft of storage space with candles and diapers)
- $9x_0 + 13x_1 + 31x_2 \geq 138$ (candles plus toilet paper rolls plus diapers must take up at least 138 square feet of storage space)
- $-2x_1 + 2x_2 \geq 0$ (-2 times the number of toilet paper rolls, plus 2 times the number of diapers should be no less than zero)
- $27x_0 + 22x_2 \leq 191$ (total combined usefulness rating from candles and diapers has to be 191 at maximum)
- $24x_1 + 22x_2 \leq 301$ (total combined usefulness rating from toilet paper rolls and diapers has to be 301 or less)
- $27x_0 + 24x_1 + 22x_2 \leq 202$ (total combined usefulness rating from candles plus toilet paper rolls plus diapers must be equal to or less than 202)
- $x_0, x_1, x_2 \geq 0$ and are integers (must have an integer number of each item)

## 5: Resource constraints
Additionally, we have:
- $27x_0 \leq 322$ 
- $24x_1 \leq 322$ 
- $22x_2 \leq 322$ 
- $9x_0 + 13x_1 + 31x_2 \leq 434$

## 6: Create the symbolic representation dictionary
The symbolic representation is:
```json
{
'sym_variables': [('x0', 'candles'), ('x1', 'toilet paper rolls'), ('x2', 'diapers')],
'objective_function': '1*x0 + 4*x1 + 7*x2',
'constraints': [
'27*x0 + 22*x2 >= 73',
'27*x0 + 24*x1 + 22*x2 >= 73',
'9*x0 + 13*x1 >= 132',
'9*x0 + 31*x2 >= 138',
'9*x0 + 13*x1 + 31*x2 >= 138',
'-2*x1 + 2*x2 >= 0',
'27*x0 + 22*x2 <= 191',
'24*x1 + 22*x2 <= 301',
'27*x0 + 24*x1 + 22*x2 <= 202',
'27*x0 <= 322',
'24*x1 <= 322',
'22*x2 <= 322',
'9*x0 + 13*x1 + 31*x2 <= 434'
]
}
```

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

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

    # Define the variables
    x0 = model.addVar(name="candles", vtype=gurobi.GRB.INTEGER)
    x1 = model.addVar(name="toilet paper rolls", vtype=gurobi.GRB.INTEGER)
    x2 = model.addVar(name="diapers", vtype=gurobi.GRB.INTEGER)

    # Define the objective function
    model.setObjective(1*x0 + 4*x1 + 7*x2, gurobi.GRB.MINIMIZE)

    # Add constraints
    model.addConstr(27*x0 + 22*x2 >= 73)
    model.addConstr(27*x0 + 24*x1 + 22*x2 >= 73)
    model.addConstr(9*x0 + 13*x1 >= 132)
    model.addConstr(9*x0 + 31*x2 >= 138)
    model.addConstr(9*x0 + 13*x1 + 31*x2 >= 138)
    model.addConstr(-2*x1 + 2*x2 >= 0)
    model.addConstr(27*x0 + 22*x2 <= 191)
    model.addConstr(24*x1 + 22*x2 <= 301)
    model.addConstr(27*x0 + 24*x1 + 22*x2 <= 202)
    model.addConstr(27*x0 <= 322)
    model.addConstr(24*x1 <= 322)
    model.addConstr(22*x2 <= 322)
    model.addConstr(9*x0 + 13*x1 + 31*x2 <= 434)

    # Update the model
    model.update()

    # Solve the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Objective: ", model.objval)
        print("Candles: ", x0.varValue)
        print("Toilet paper rolls: ", x1.varValue)
        print("Diapers: ", x2.varValue)
    else:
        print("The model is infeasible")

solve_optimization_problem()
```