## Step 1: Define the symbolic representation of the variables
The variables are 'dish soap bottles' and 'rubber gloves', which can be represented symbolically as $x_1$ and $x_2$ respectively.

## Step 2: Convert the natural language description into a symbolic representation
The objective function to maximize is $5x_1 + 7x_2$.

## Step 3: List all the constraints in symbolic notation
The constraints are:
1. $5.18x_1 + 7.26x_2 \geq 5$
2. $5.67x_1 + 6.51x_2 \geq 11$
3. $4x_1 - 7x_2 \geq 0$
4. $5.18x_1 + 7.26x_2 \leq 22$
5. $5.67x_1 + 6.51x_2 \leq 33$

## 4: Consider the bounds and integrality constraints
- $x_1$ and $x_2$ must be integers.
- The upper bounds for $r_0$ (dollar value) and $r_1$ (storage space) are 22 and 33 respectively.

## 5: Formulate the symbolic representation of the problem
```json
{
'sym_variables': [('x1', 'dish soap bottles'), ('x2', 'rubber gloves')],
'objective_function': '5*x1 + 7*x2',
'constraints': [
    '5.18*x1 + 7.26*x2 >= 5',
    '5.67*x1 + 6.51*x2 >= 11',
    '4*x1 - 7*x2 >= 0',
    '5.18*x1 + 7.26*x2 <= 22',
    '5.67*x1 + 6.51*x2 <= 33'
]
}
```

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

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

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

    # Objective function
    model.setObjective(5 * x1 + 7 * x2, gurobi.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(5.18 * x1 + 7.26 * x2 >= 5, name="total_value_min")
    model.addConstr(5.67 * x1 + 6.51 * x2 >= 11, name="storage_space_min")
    model.addConstr(4 * x1 - 7 * x2 >= 0, name="dish_soap_vs_rubber_gloves")
    model.addConstr(5.18 * x1 + 7.26 * x2 <= 22, name="total_value_max")
    model.addConstr(5.67 * x1 + 6.51 * x2 <= 33, name="storage_space_max")

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"dish soap bottles: {x1.varValue}")
        print(f"rubber gloves: {x2.varValue}")
        print(f"Objective: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```