## Step 1: Define the symbolic representation of the variables
The variables are 'hours worked by Jean', 'hours worked by Peggy', 'hours worked by Paul', and 'hours worked by George', which can be represented symbolically as $x_0$, $x_1$, $x_2$, and $x_3$ respectively.

## Step 2: Define the objective function in symbolic notation
The objective function to minimize is $5.08x_0 + 8.0x_1 + 2.06x_2 + 6.52x_3$.

## Step 3: List the constraints in symbolic notation
The constraints are:
1. $2x_0 \leq 470$ (computer competence rating for Jean, but it's given as a fixed value, so $2x_0 = 2$)
2. $15x_0 \leq 234$ (paperwork competence rating for Jean, but it's given as a fixed value, so $15x_0 = 15$)
3. $18x_1 \leq 470$ (computer competence rating for Peggy, but it's given as a fixed value, so $18x_1 = 18$)
4. $23x_1 \leq 234$ (paperwork competence rating for Peggy, but it's given as a fixed value, so $23x_1 = 23$)
5. $28x_2 \leq 470$ (computer competence rating for Paul, but it's given as a fixed value, so $28x_2 = 28$)
6. $26x_2 \leq 234$ (paperwork competence rating for Paul, but it's given as a fixed value, so $26x_2 = 26$)
7. $6x_3 \leq 470$ (computer competence rating for George, but it's given as a fixed value, so $6x_3 = 6$)
8. $9x_3 \leq 234$ (paperwork competence rating for George, but it's given as a fixed value, so $9x_3 = 9$)
9. $28x_2 + 6x_3 \geq 74$
10. $2x_0 + 18x_1 \geq 78$
11. $2x_0 + 28x_2 + 6x_3 \geq 73$
12. $2x_0 + 18x_1 + 28x_2 + 6x_3 \geq 73$
13. $15x_0 + 23x_1 \geq 40$
14. $23x_1 + 26x_2 \geq 29$
15. $15x_0 + 9x_3 \geq 41$
16. $15x_0 + 23x_1 + 26x_2 + 9x_3 \geq 41$
17. $8x_2 - 5x_3 \geq 0$
18. $18x_1 + 28x_2 \leq 252$
19. $18x_1 + 6x_3 \leq 330$
20. $28x_2 + 6x_3 \leq 271$
21. $2x_0 + 18x_1 \leq 468$
22. $2x_0 + 6x_3 \leq 355$
23. $18x_1 + 28x_2 + 6x_3 \leq 282$
24. $2x_0 + 18x_1 + 28x_2 \leq 235$
25. $23x_1 + 26x_2 + 9x_3 \leq 128$
26. $15x_0 + 26x_2 + 9x_3 \leq 214$

## Step 4: Correcting the Understanding of Constraints
Upon closer inspection, constraints 1 through 8 are not actually constraints but rather given attributes. The actual constraints are from 9 to 26.

## 5: Symbolic Representation
The symbolic representation of the problem is:
```json
{
'sym_variables': [('x0', 'hours worked by Jean'), ('x1', 'hours worked by Peggy'), ('x2', 'hours worked by Paul'), ('x3', 'hours worked by George')],
'objective_function': '5.08*x0 + 8.0*x1 + 2.06*x2 + 6.52*x3',
'constraints': [
'28*x2 + 6*x3 >= 74',
'2*x0 + 18*x1 >= 78',
'2*x0 + 28*x2 + 6*x3 >= 73',
'2*x0 + 18*x1 + 28*x2 + 6*x3 >= 73',
'15*x0 + 23*x1 >= 40',
'23*x1 + 26*x2 >= 29',
'15*x0 + 9*x3 >= 41',
'15*x0 + 23*x1 + 26*x2 + 9*x3 >= 41',
'8*x2 - 5*x3 >= 0',
'18*x1 + 28*x2 <= 252',
'18*x1 + 6*x3 <= 330',
'28*x2 + 6*x3 <= 271',
'2*x0 + 18*x1 <= 468',
'2*x0 + 6*x3 <= 355',
'18*x1 + 28*x2 + 6*x3 <= 282',
'2*x0 + 18*x1 + 28*x2 <= 235',
'23*x1 + 26*x2 + 9*x3 <= 128',
'15*x0 + 26*x2 + 9*x3 <= 214'
]
}
```

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

def solve_optimization_problem():
    model = gurobi.Model()
    
    # Define variables
    x0 = model.addVar(name="x0", lb=0)  # hours worked by Jean
    x1 = model.addVar(name="x1", lb=0)  # hours worked by Peggy
    x2 = model.addVar(name="x2", lb=0)  # hours worked by Paul
    x3 = model.addVar(name="x3", lb=0)  # hours worked by George

    # Objective function
    model.setObjective(5.08*x0 + 8.0*x1 + 2.06*x2 + 6.52*x3, gurobi.GRB.MINIMIZE)

    # Constraints
    model.addConstr(28*x2 + 6*x3 >= 74)
    model.addConstr(2*x0 + 18*x1 >= 78)
    model.addConstr(2*x0 + 28*x2 + 6*x3 >= 73)
    model.addConstr(2*x0 + 18*x1 + 28*x2 + 6*x3 >= 73)
    model.addConstr(15*x0 + 23*x1 >= 40)
    model.addConstr(23*x1 + 26*x2 >= 29)
    model.addConstr(15*x0 + 9*x3 >= 41)
    model.addConstr(15*x0 + 23*x1 + 26*x2 + 9*x3 >= 41)
    model.addConstr(8*x2 - 5*x3 >= 0)
    model.addConstr(18*x1 + 28*x2 <= 252)
    model.addConstr(18*x1 + 6*x3 <= 330)
    model.addConstr(28*x2 + 6*x3 <= 271)
    model.addConstr(2*x0 + 18*x1 <= 468)
    model.addConstr(2*x0 + 6*x3 <= 355)
    model.addConstr(18*x1 + 28*x2 + 6*x3 <= 282)
    model.addConstr(2*x0 + 18*x1 + 28*x2 <= 235)
    model.addConstr(23*x1 + 26*x2 + 9*x3 <= 128)
    model.addConstr(15*x0 + 26*x2 + 9*x3 <= 214)

    # 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")

solve_optimization_problem()
```