## Step 1: Define the symbolic representation of the variables
The variables are 'hours worked by Dale', 'hours worked by Paul', and 'hours worked by Bobby', 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 $2.43x_0 + 3.2x_1 + 3.04x_2$.

## Step 3: List the constraints in symbolic notation
The constraints are:
- $16x_0 + 14x_1 + 16x_2 \geq 91$
- $15x_0 + 10x_1 \geq 54$
- $10x_1 + 21x_2 \geq 44$
- $16x_0 + 16x_2 \leq 166$
- $14x_1 + 16x_2 \leq 119$
- $16x_0 + 14x_1 + 16x_2 \leq 132$
- $15x_0 + 10x_1 + 21x_2 \leq 205$
- $15x_0 + 21x_2 \leq 144$
- $15x_0 + 10x_1 \leq 239$

## 4: Create a symbolic representation of the problem
The symbolic representation is:
```json
{
'sym_variables': [('x0', 'hours worked by Dale'), ('x1', 'hours worked by Paul'), ('x2', 'hours worked by Bobby')],
'objective_function': '2.43*x0 + 3.2*x1 + 3.04*x2',
'constraints': [
'16*x0 + 14*x1 + 16*x2 >= 91',
'15*x0 + 10*x1 >= 54',
'10*x1 + 21*x2 >= 44',
'16*x0 + 16*x2 <= 166',
'14*x1 + 16*x2 <= 119',
'16*x0 + 14*x1 + 16*x2 <= 132',
'15*x0 + 10*x1 + 21*x2 <= 205',
'15*x0 + 21*x2 <= 144',
'15*x0 + 10*x1 <= 239'
]
}
```

## 5: 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='hours_worked_by_Dale', lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY)
    x1 = model.addVar(name='hours_worked_by_Paul', lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY)
    x2 = model.addVar(name='hours_worked_by_Bobby', lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY)

    # Define the objective function
    model.setObjective(2.43*x0 + 3.2*x1 + 3.04*x2, gurobi.GRB.MAXIMIZE)

    # Add constraints
    model.addConstr(16*x0 + 14*x1 + 16*x2 >= 91)
    model.addConstr(15*x0 + 10*x1 >= 54)
    model.addConstr(10*x1 + 21*x2 >= 44)
    model.addConstr(16*x0 + 16*x2 <= 166)
    model.addConstr(14*x1 + 16*x2 <= 119)
    model.addConstr(16*x0 + 14*x1 + 16*x2 <= 132)
    model.addConstr(15*x0 + 10*x1 + 21*x2 <= 205)
    model.addConstr(15*x0 + 21*x2 <= 144)
    model.addConstr(15*x0 + 10*x1 <= 239)

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print('Optimal solution found.')
        print(f'Hours worked by Dale: {x0.varValue}')
        print(f'Hours worked by Paul: {x1.varValue}')
        print(f'Hours worked by Bobby: {x2.varValue}')
        print(f'Objective function value: {model.objVal}')
    else:
        print('No optimal solution found.')

solve_optimization_problem()
```