## Step 1: Define the symbolic representation of the variables
The variables are 'hours worked by Dale', 'hours worked by Bill', and 'hours worked by George', 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:
- 'r0': {'description': 'dollar cost per hour', 'upper_bound': 333, 'x0': 9, 'x1': 17, 'x2': 4}
- 'r1': {'description': 'likelihood to quit index', 'upper_bound': 491, 'x0': 6, 'x1': 16, 'x2': 3}
- 'r2': {'description': 'computer competence rating', 'upper_bound': 426, 'x0': 6, 'x1': 8, 'x2': 4}

## 3: Define the objective function in symbolic notation
The objective function to minimize is $4.18x_0 + 2.77x_1 + 9.56x_2$.

## 4: List all the constraints in symbolic notation
The constraints are:
1. $9x_0 + 17x_1 \geq 54$
2. $9x_0 + 17x_1 + 4x_2 \geq 54$
3. $6x_0 + 16x_1 \geq 58$
4. $6x_0 + 3x_2 \geq 79$
5. $6x_0 + 16x_1 + 3x_2 \geq 79$
6. $6x_0 + 4x_2 \geq 107$
7. $8x_1 + 4x_2 \geq 137$
8. $6x_0 + 8x_1 + 4x_2 \geq 137$
9. $-2x_1 + 6x_2 \geq 0$
10. $9x_0 + 4x_2 \leq 127$
11. $17x_1 + 4x_2 \leq 202$
12. $9x_0 + 17x_1 \leq 286$
13. $6x_0 + 16x_1 \leq 293$
14. $16x_1 + 3x_2 \leq 393$

## 5: Create a symbolic representation of the problem
```json
{
'sym_variables': [('x0', 'hours worked by Dale'), ('x1', 'hours worked by Bill'), ('x2', 'hours worked by George')],
'objective_function': '4.18*x0 + 2.77*x1 + 9.56*x2',
'constraints': [
'9*x0 + 17*x1 >= 54',
'9*x0 + 17*x1 + 4*x2 >= 54',
'6*x0 + 16*x1 >= 58',
'6*x0 + 3*x2 >= 79',
'6*x0 + 16*x1 + 3*x2 >= 79',
'6*x0 + 4*x2 >= 107',
'8*x1 + 4*x2 >= 137',
'6*x0 + 8*x1 + 4*x2 >= 137',
'-2*x1 + 6*x2 >= 0',
'9*x0 + 4*x2 <= 127',
'17*x1 + 4*x2 <= 202',
'9*x0 + 17*x1 <= 286',
'6*x0 + 16*x1 <= 293',
'16*x1 + 3*x2 <= 393'
]
}
```

## 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 Dale
    x1 = model.addVar(name="x1", lb=0)  # hours worked by Bill
    x2 = model.addVar(name="x2", lb=0)  # hours worked by George

    # Define the objective function
    model.setObjective(4.18 * x0 + 2.77 * x1 + 9.56 * x2, gurobi.GRB.MINIMIZE)

    # Add constraints
    model.addConstr(9 * x0 + 17 * x1 >= 54)
    model.addConstr(9 * x0 + 17 * x1 + 4 * x2 >= 54)
    model.addConstr(6 * x0 + 16 * x1 >= 58)
    model.addConstr(6 * x0 + 3 * x2 >= 79)
    model.addConstr(6 * x0 + 16 * x1 + 3 * x2 >= 79)
    model.addConstr(6 * x0 + 4 * x2 >= 107)
    model.addConstr(8 * x1 + 4 * x2 >= 137)
    model.addConstr(6 * x0 + 8 * x1 + 4 * x2 >= 137)
    model.addConstr(-2 * x1 + 6 * x2 >= 0)
    model.addConstr(9 * x0 + 4 * x2 <= 127)
    model.addConstr(17 * x1 + 4 * x2 <= 202)
    model.addConstr(9 * x0 + 17 * x1 <= 286)
    model.addConstr(6 * x0 + 16 * x1 <= 293)
    model.addConstr(16 * x1 + 3 * x2 <= 393)

    # Optimize the model
    model.optimize()

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

solve_optimization_problem()
```