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

## Step 2: Convert the given attributes into a usable format
The given attributes are:
- $r_0$: productivity rating with $x_0 = 13$, $x_1 = 7$, $x_2 = 8$ and an upper bound of 44.
- $r_1$: work quality rating with $x_0 = 14$, $x_1 = 3$, $x_2 = 14$ and an upper bound of 72.
- $r_2$: organization score with $x_0 = 5$, $x_1 = 14$, $x_2 = 5$ and an upper bound of 59.

## 3: Define the objective function
The objective function to minimize is $3.01x_0 + 8.33x_1 + 4.51x_2$.

## 4: List all the constraints
1. $13x_0 \leq 44$
2. $14x_0 \leq 72$
3. $5x_0 \leq 59$
4. $7x_1 \leq 44$
5. $3x_1 \leq 72$
6. $14x_1 \leq 59$
7. $8x_2 \leq 44$
8. $14x_2 \leq 72$
9. $5x_2 \leq 59$
10. $13x_0 + 7x_1 \geq 4$
11. $13x_0 + 7x_1 + 8x_2 \geq 4$
12. $14x_0 + 3x_1 \geq 15$
13. $3x_1 + 14x_2 \geq 11$
14. $14x_0 + 3x_1 + 14x_2 \geq 19$
15. $5x_0 + 14x_1 \geq 17$
16. $5x_0 + 14x_1 + 5x_2 \geq 9$
17. $-9x_0 + x_2 \geq 0$
18. $8x_1 - x_2 \geq 0$
19. $8x_0 - 4x_1 \geq 0$
20. $13x_0 + 8x_2 \leq 30$
21. $13x_0 + 7x_1 \leq 41$
22. $13x_0 + 7x_1 + 8x_2 \leq 42$
23. $14x_1 + 5x_2 \leq 33$
24. $5x_0 + 5x_2 \leq 39$

## 5: Create a symbolic representation of the problem
```json
{
    'sym_variables': [
        ('x0', 'hours worked by Laura'), 
        ('x1', 'hours worked by Dale'), 
        ('x2', 'hours worked by John')
    ], 
    'objective_function': '3.01*x0 + 8.33*x1 + 4.51*x2', 
    'constraints': [
        '13*x0 <= 44',
        '14*x0 <= 72',
        '5*x0 <= 59',
        '7*x1 <= 44',
        '3*x1 <= 72',
        '14*x1 <= 59',
        '8*x2 <= 44',
        '14*x2 <= 72',
        '5*x2 <= 59',
        '13*x0 + 7*x1 >= 4',
        '13*x0 + 7*x1 + 8*x2 >= 4',
        '14*x0 + 3*x1 >= 15',
        '3*x1 + 14*x2 >= 11',
        '14*x0 + 3*x1 + 14*x2 >= 19',
        '5*x0 + 14*x1 >= 17',
        '5*x0 + 14*x1 + 5*x2 >= 9',
        '-9*x0 + x2 >= 0',
        '8*x1 - x2 >= 0',
        '8*x0 - 4*x1 >= 0',
        '13*x0 + 8*x2 <= 30',
        '13*x0 + 7*x1 <= 41',
        '13*x0 + 7*x1 + 8*x2 <= 42',
        '14*x1 + 5*x2 <= 33',
        '5*x0 + 5*x2 <= 39'
    ]
}
```

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

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

    # Define the variables
    x0 = model.addVar(lb=0, name="hours_worked_by_Laura", vtype=gurobi.GRB.CONTINUOUS)
    x1 = model.addVar(lb=0, name="hours_worked_by_Dale", vtype=gurobi.GRB.CONTINUOUS)
    x2 = model.addVar(lb=0, name="hours_worked_by_John", vtype=gurobi.GRB.CONTINUOUS)

    # Define the objective function
    model.setObjective(3.01*x0 + 8.33*x1 + 4.51*x2, gurobi.GRB.MINIMIZE)

    # Add constraints
    model.addConstr(13*x0 <= 44)
    model.addConstr(14*x0 <= 72)
    model.addConstr(5*x0 <= 59)
    model.addConstr(7*x1 <= 44)
    model.addConstr(3*x1 <= 72)
    model.addConstr(14*x1 <= 59)
    model.addConstr(8*x2 <= 44)
    model.addConstr(14*x2 <= 72)
    model.addConstr(5*x2 <= 59)
    model.addConstr(13*x0 + 7*x1 >= 4)
    model.addConstr(13*x0 + 7*x1 + 8*x2 >= 4)
    model.addConstr(14*x0 + 3*x1 >= 15)
    model.addConstr(3*x1 + 14*x2 >= 11)
    model.addConstr(14*x0 + 3*x1 + 14*x2 >= 19)
    model.addConstr(5*x0 + 14*x1 >= 17)
    model.addConstr(5*x0 + 14*x1 + 5*x2 >= 9)
    model.addConstr(-9*x0 + x2 >= 0)
    model.addConstr(8*x1 - x2 >= 0)
    model.addConstr(8*x0 - 4*x1 >= 0)
    model.addConstr(13*x0 + 8*x2 <= 30)
    model.addConstr(13*x0 + 7*x1 <= 41)
    model.addConstr(13*x0 + 7*x1 + 8*x2 <= 42)
    model.addConstr(14*x1 + 5*x2 <= 33)
    model.addConstr(5*x0 + 5*x2 <= 39)

    # Solve the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Objective: ", model.objval)
        print("Hours worked by Laura: ", x0.varValue)
        print("Hours worked by Dale: ", x1.varValue)
        print("Hours worked by John: ", x2.varValue)
    else:
        print("The model is infeasible")

solve_optimization_problem()
```