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

## Step 2: Define the objective function in symbolic notation
The objective function to maximize is $6.73x_0 + 3.92x_1 + 1.71x_2$.

## Step 3: Define the constraints in symbolic notation
The constraints are:
- $20x_0 \geq 20$ is not correct, it is an equality for individual, so $20 = 20$ 
- $28x_0$ has an upper bound but is not a constraint by itself.
- $11x_0$ has an upper bound but is not a constraint by itself.
- Similarly, individual constraints for Bobby and George are equalities and not constraints.
- $20x_0 + 27x_1 \geq 55$
- $11x_0 + 6x_2 \geq 44$
- $21x_1 + 6x_2 \geq 26$
- $11x_0 + 21x_1 + 6x_2 \geq 36$
- $20x_0 + 27x_1 \leq 248$
- $27x_1 + 19x_2 \leq 231$
- $20x_0 + 27x_1 + 19x_2 \leq 330$
- $28x_0 + 32x_1 \leq 226$
- $32x_1 + 23x_2 \leq 126$
- $28x_0 + 32x_1 + 23x_2 \leq 248$
- $11x_0 + 21x_1 \leq 89$
- $11x_0 + 6x_2 \leq 88$
- $11x_0 + 21x_1 + 6x_2 \leq 74$

However, we need to correct and simplify the constraints based on the problem description:
- The individual likelihood to quit, dollar cost per hour, and computer competence rating are given and not constraints but attributes.
- The actual constraints are the combined ones.

## 4: Formulate the symbolic representation of the problem
```json
{
'sym_variables': [('x0', 'hours worked by Laura'), ('x1', 'hours worked by Bobby'), ('x2', 'hours worked by George')],
'objective_function': '6.73*x0 + 3.92*x1 + 1.71*x2',
'constraints': [
    '20*x0 + 27*x1 >= 55',
    '11*x0 + 6*x2 >= 44',
    '21*x1 + 6*x2 >= 26',
    '11*x0 + 21*x1 + 6*x2 >= 36',
    '20*x0 + 27*x1 <= 248',
    '27*x1 + 19*x2 <= 231',
    '20*x0 + 27*x1 + 19*x2 <= 330',
    '28*x0 + 32*x1 <= 226',
    '32*x1 + 23*x2 <= 126',
    '28*x0 + 32*x1 + 23*x2 <= 248',
    '11*x0 + 21*x1 <= 89',
    '11*x0 + 6*x2 <= 88',
    '11*x0 + 21*x1 + 6*x2 <= 74'
]
}
```

## 5: Implement the problem in Gurobi
```python
import gurobi

def solve_optimization_problem():
    model = gurobi.Model()
    
    # Define variables
    x0 = model.addVar(lb=0, name='hours_worked_by_Laura')
    x1 = model.addVar(lb=0, name='hours_worked_by_Bobby')
    x2 = model.addVar(lb=0, name='hours_worked_by_George')
    
    # Define objective function
    model.setObjective(6.73*x0 + 3.92*x1 + 1.71*x2, gurobi.GRB.MAXIMIZE)
    
    # Define constraints
    model.addConstr(20*x0 + 27*x1 >= 55, name='likelihood_to_quit_Laura_Bobby')
    model.addConstr(11*x0 + 6*x2 >= 44, name='computer_competence_Laura_George')
    model.addConstr(21*x1 + 6*x2 >= 26, name='computer_competence_Bobby_George')
    model.addConstr(11*x0 + 21*x1 + 6*x2 >= 36, name='computer_competence_all')
    model.addConstr(20*x0 + 27*x1 <= 248, name='likelihood_to_quit_Laura_Bobby_max')
    model.addConstr(27*x1 + 19*x2 <= 231, name='likelihood_to_quit_Bobby_George_max')
    model.addConstr(20*x0 + 27*x1 + 19*x2 <= 330, name='likelihood_to_quit_all_max')
    model.addConstr(28*x0 + 32*x1 <= 226, name='dollar_cost_Laura_Bobby_max')
    model.addConstr(32*x1 + 23*x2 <= 126, name='dollar_cost_Bobby_George_max')
    model.addConstr(28*x0 + 32*x1 + 23*x2 <= 248, name='dollar_cost_all_max')
    model.addConstr(11*x0 + 21*x1 <= 89, name='computer_competence_Laura_Bobby_max')
    model.addConstr(11*x0 + 6*x2 <= 88, name='computer_competence_Laura_George_max')
    model.addConstr(11*x0 + 21*x1 + 6*x2 <= 74, name='computer_competence_all_max')
    
    # Solve the model
    model.optimize()
    
    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print('Objective: ', model.objval)
        print('Hours worked by Laura: ', x0.x)
        print('Hours worked by Bobby: ', x1.x)
        print('Hours worked by George: ', x2.x)
    else:
        print('The model is infeasible')

solve_optimization_problem()
```