## Step 1: Define the symbolic representation of the variables
The variables are 'hours worked by Peggy', 'hours worked by Mary', and 'hours worked by Jean', which can be represented symbolically as $x_0$, $x_1$, and $x_2$ respectively.

## Step 2: Convert the given resources/attributes into a usable format
The resources/attributes given are:
- $r_0$: {'description': 'dollar cost per hour', 'upper_bound': 38, 'x0': 12, 'x1': 9, 'x2': 1}
- $r_1$: {'description': 'productivity rating', 'upper_bound': 92, 'x0': 2, 'x1': 1, 'x2': 10}

## 3: Define the objective function in symbolic notation
The objective function to minimize is $8x_0 + 2x_1 + 3x_2$.

## 4: List all the constraints in symbolic notation
The constraints are:
1. $9x_1 + x_2 \geq 7$
2. $12x_0 + 9x_1 + x_2 \geq 7$
3. $2x_0 + x_1 \geq 10$
4. $x_1 + 10x_2 \geq 13$
5. $2x_0 + 10x_2 \geq 16$
6. $2x_0 + x_1 + 10x_2 \geq 16$
7. $-8x_0 + 5x_1 \geq 0$
8. $8x_1 - 7x_2 \geq 0$
9. $2x_0 + x_1 \leq 32$

## 5: Create a symbolic representation of the problem
```json
{
'sym_variables': [('x0', 'hours worked by Peggy'), ('x1', 'hours worked by Mary'), ('x2', 'hours worked by Jean')],
'objective_function': '8*x0 + 2*x1 + 3*x2',
'constraints': [
'9*x1 + x2 >= 7',
'12*x0 + 9*x1 + x2 >= 7',
'2*x0 + x1 >= 10',
'x1 + 10*x2 >= 13',
'2*x0 + 10*x2 >= 16',
'2*x0 + x1 + 10*x2 >= 16',
'-8*x0 + 5*x1 >= 0',
'8*x1 - 7*x2 >= 0',
'2*x0 + x1 <= 32'
]
}
```

## 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
    x0 = model.addVar(name='x0', lb=0)  # hours worked by Peggy
    x1 = model.addVar(name='x1', lb=0)  # hours worked by Mary
    x2 = model.addVar(name='x2', lb=0)  # hours worked by Jean

    # Define the objective function
    model.setObjective(8*x0 + 2*x1 + 3*x2, gurobi.GRB.MINIMIZE)

    # Add constraints
    model.addConstr(9*x1 + x2 >= 7)
    model.addConstr(12*x0 + 9*x1 + x2 >= 7)
    model.addConstr(2*x0 + x1 >= 10)
    model.addConstr(x1 + 10*x2 >= 13)
    model.addConstr(2*x0 + 10*x2 >= 16)
    model.addConstr(2*x0 + x1 + 10*x2 >= 16)
    model.addConstr(-8*x0 + 5*x1 >= 0)
    model.addConstr(8*x1 - 7*x2 >= 0)
    model.addConstr(2*x0 + x1 <= 32)

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print('Objective: ', model.objVal)
        print('Hours worked by Peggy: ', x0.varValue)
        print('Hours worked by Mary: ', x1.varValue)
        print('Hours worked by Jean: ', x2.varValue)
    else:
        print('The model is infeasible')

solve_optimization_problem()
```