## Step 1: Define the symbolic representation of the variables
The variables are ['hours worked by Bill', 'hours worked by Dale', 'hours worked by Peggy', 'hours worked by Jean']. Let's denote them as $x_0$, $x_1$, $x_2$, and $x_3$ respectively.

## Step 2: Define the attributes for each variable
The attributes are given as:
- $r_0$: work quality rating
- $r_1$: dollar cost per hour
- $r_2$: computer competence rating
- $r_3$: organization score

With values:
- $x_0$: $r_0 = 3$, $r_1 = 4$, $r_2 = 1$, $r_3 = 3$
- $x_1$: $r_0 = 1$, $r_1 = 5$, $r_2 = 4$, $r_3 = 3$
- $x_2$: $r_0 = 2$, $r_1 = 5$, $r_2 = 3$, $r_3 = 4$
- $x_3$: $r_0 = 2$, $r_1 = 5$, $r_2 = 1$, $r_3 = 2$

## Step 3: Formulate the objective function
The objective function to minimize is $9.09x_0 + 3.98x_1 + 5.26x_2 + 8.66x_3$.

## 4: Formulate the constraints
1. $3x_0 \geq 6$ (work quality rating from Bill and Dale)
2. $3x_0 + x_1 + 2x_2 + 2x_3 \geq 6$ (total work quality rating)
3. $4x_0 + 5x_2 \geq 9$ (dollar cost per hour from Bill and Peggy)
4. $4x_0 + 5x_1 \geq 13$ (dollar cost per hour from Bill and Dale)
5. $4x_0 + 5x_1 + 5x_2 + 5x_3 \geq 13$ (total dollar cost per hour)
6. $x_0 + 3x_2 \geq 12$ (computer competence rating from Bill and Peggy)
7. $4x_1 + 3x_2 \geq 10$ (computer competence rating from Dale and Peggy)
8. $3x_2 + x_3 \geq 8$ (computer competence rating from Peggy and Jean)
9. $4x_1 + x_3 \geq 8$ (computer competence rating from Dale and Jean)
10. $x_0 + x_3 \geq 11$ (computer competence rating from Bill and Jean)
11. $x_0 + 4x_1 \geq 7$ (computer competence rating from Bill and Dale)
12. $x_0 + 4x_1 + 3x_2 + x_3 \geq 7$ (total computer competence rating)
13. $3x_1 + 2x_3 \geq 8$ (organization score from Dale and Jean)
14. $3x_0 + 4x_2 \geq 11$ (organization score from Bill and Peggy)
15. $3x_0 + x_1 + 4x_2 \geq 5$ (organization score from Bill, Dale, and Peggy)
16. $3x_0 + 4x_2 + 2x_3 \geq 5$ (organization score from Bill, Peggy, and Jean)
17. $3x_0 + x_1 + 4x_2 \geq 8$ (organization score from Bill, Dale, and Peggy)
18. $3x_0 + x_1 + 4x_2 + 2x_3 \geq 8$ (total organization score)
19. $-9x_1 + x_3 \geq 0$
20. $9x_0 - 10x_1 \geq 0$
21. $3x_0 + 2x_2 \leq 28$ (work quality rating from Bill and Peggy)
22. $3x_0 + 2x_3 \leq 15$ (work quality rating from Bill and Jean)
23. $3x_0 + 2x_2 + 2x_3 \leq 13$ (work quality rating from Bill, Peggy, and Jean)
24. $3x_0 + x_1 + 2x_2 \leq 32$ (work quality rating from Bill, Dale, and Peggy)
25. $x_0 + x_3 \leq 81$ (computer competence rating from Bill and Jean)
26. $3x_2 + x_3 \leq 84$ (computer competence rating from Peggy and Jean)
27. $x_0 + 3x_2 \leq 65$ (computer competence rating from Bill and Peggy)
28. $4x_1 + 3x_2 \leq 46$ (computer competence rating from Dale and Peggy)
29. $3x_0 + x_1 + 4x_2 \leq 38$ (organization score from Bill, Dale, and Peggy)

## 5: Symbolic Representation
```json
{
    'sym_variables': [
        ('x0', 'hours worked by Bill'),
        ('x1', 'hours worked by Dale'),
        ('x2', 'hours worked by Peggy'),
        ('x3', 'hours worked by Jean')
    ],
    'objective_function': '9.09*x0 + 3.98*x1 + 5.26*x2 + 8.66*x3',
    'constraints': [
        '3*x0 >= 6',
        '3*x0 + x1 + 2*x2 + 2*x3 >= 6',
        '4*x0 + 5*x2 >= 9',
        '4*x0 + 5*x1 >= 13',
        '4*x0 + 5*x1 + 5*x2 + 5*x3 >= 13',
        'x0 + 3*x2 >= 12',
        '4*x1 + 3*x2 >= 10',
        '3*x2 + x3 >= 8',
        '4*x1 + x3 >= 8',
        'x0 + x3 >= 11',
        'x0 + 4*x1 >= 7',
        'x0 + 4*x1 + 3*x2 + x3 >= 7',
        '3*x1 + 2*x3 >= 8',
        '3*x0 + 4*x2 >= 11',
        '3*x0 + x1 + 4*x2 >= 5',
        '3*x0 + 4*x2 + 2*x3 >= 5',
        '3*x0 + x1 + 4*x2 >= 8',
        '3*x0 + x1 + 4*x2 + 2*x3 >= 8',
        '-9*x1 + x3 >= 0',
        '9*x0 - 10*x1 >= 0',
        '3*x0 + 2*x2 <= 28',
        '3*x0 + 2*x3 <= 15',
        '3*x0 + 2*x2 + 2*x3 <= 13',
        '3*x0 + x1 + 2*x2 <= 32',
        'x0 + x3 <= 81',
        '3*x2 + x3 <= 84',
        'x0 + 3*x2 <= 65',
        '4*x1 + 3*x2 <= 46',
        '3*x0 + x1 + 4*x2 <= 38'
    ]
}
```

## 6: Gurobi Code
```python
import gurobi

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

    # Define variables
    x0 = model.addVar(name="x0", lb=0)  # hours worked by Bill
    x1 = model.addVar(name="x1", lb=0)  # hours worked by Dale
    x2 = model.addVar(name="x2", lb=0)  # hours worked by Peggy
    x3 = model.addVar(name="x3", lb=0)  # hours worked by Jean

    # Objective function
    model.setObjective(9.09 * x0 + 3.98 * x1 + 5.26 * x2 + 8.66 * x3, gurobi.GRB.MINIMIZE)

    # Constraints
    model.addConstr(3 * x0 >= 6)
    model.addConstr(3 * x0 + x1 + 2 * x2 + 2 * x3 >= 6)
    model.addConstr(4 * x0 + 5 * x2 >= 9)
    model.addConstr(4 * x0 + 5 * x1 >= 13)
    model.addConstr(4 * x0 + 5 * x1 + 5 * x2 + 5 * x3 >= 13)
    model.addConstr(x0 + 3 * x2 >= 12)
    model.addConstr(4 * x1 + 3 * x2 >= 10)
    model.addConstr(3 * x2 + x3 >= 8)
    model.addConstr(4 * x1 + x3 >= 8)
    model.addConstr(x0 + x3 >= 11)
    model.addConstr(x0 + 4 * x1 >= 7)
    model.addConstr(x0 + 4 * x1 + 3 * x2 + x3 >= 7)
    model.addConstr(3 * x1 + 2 * x3 >= 8)
    model.addConstr(3 * x0 + 4 * x2 >= 11)
    model.addConstr(3 * x0 + x1 + 4 * x2 >= 5)
    model.addConstr(3 * x0 + 4 * x2 + 2 * x3 >= 5)
    model.addConstr(3 * x0 + x1 + 4 * x2 >= 8)
    model.addConstr(3 * x0 + x1 + 4 * x2 + 2 * x3 >= 8)
    model.addConstr(-9 * x1 + x3 >= 0)
    model.addConstr(9 * x0 - 10 * x1 >= 0)
    model.addConstr(3 * x0 + 2 * x2 <= 28)
    model.addConstr(3 * x0 + 2 * x3 <= 15)
    model.addConstr(3 * x0 + 2 * x2 + 2 * x3 <= 13)
    model.addConstr(3 * x0 + x1 + 2 * x2 <= 32)
    model.addConstr(x0 + x3 <= 81)
    model.addConstr(3 * x2 + x3 <= 84)
    model.addConstr(x0 + 3 * x2 <= 65)
    model.addConstr(4 * x1 + 3 * x2 <= 46)
    model.addConstr(3 * x0 + x1 + 4 * x2 <= 38)

    # Optimize
    model.optimize()

    # Print solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Objective: ", model.objval)
        print("x0: ", x0.varValue)
        print("x1: ", x1.varValue)
        print("x2: ", x2.varValue)
        print("x3: ", x3.varValue)
    else:
        print("No solution found")

optimization_problem()
```