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

## Step 2: Define the objective function in symbolic notation
The objective function to maximize is $6.13x_1 + 3.02x_2$.

## Step 3: Define the constraints in symbolic notation
The constraints given are:
- $4.87x_1 + 1.72x_2 \geq 41$
- $0.28x_1 + 6.84x_2 \geq 18$
- $6.33x_1 + 4.33x_2 \geq 17$
- $5.0x_1 + 3.52x_2 \geq 110$
- $-x_1 + 6x_2 \geq 0$
- $4.87x_1 + 1.72x_2 \leq 92$
- $0.28x_1 + 6.84x_2 \leq 58$
- $6.33x_1 + 4.33x_2 \leq 37$
- $5.0x_1 + 3.52x_2 \leq 231$

## 4: Create a symbolic representation of the problem
The symbolic representation is:
```json
{
'sym_variables': [('x1', 'hours worked by Laura'), ('x2', 'hours worked by Dale')],
'objective_function': '6.13*x1 + 3.02*x2',
'constraints': [
    '4.87*x1 + 1.72*x2 >= 41',
    '0.28*x1 + 6.84*x2 >= 18',
    '6.33*x1 + 4.33*x2 >= 17',
    '5.0*x1 + 3.52*x2 >= 110',
    '-1*x1 + 6*x2 >= 0',
    '4.87*x1 + 1.72*x2 <= 92',
    '0.28*x1 + 6.84*x2 <= 58',
    '6.33*x1 + 4.33*x2 <= 37',
    '5.0*x1 + 3.52*x2 <= 231'
]
}
```

## 5: Implement the optimization problem using Gurobi
```python
import gurobi

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

    # Define the variables
    x1 = model.addVar(name='hours_worked_by_Laura', lb=0)
    x2 = model.addVar(name='hours_worked_by_Dale', lb=0)

    # Define the objective function
    model.setObjective(6.13*x1 + 3.02*x2, gurobi.GRB.MAXIMIZE)

    # Define the constraints
    model.addConstr(4.87*x1 + 1.72*x2 >= 41)
    model.addConstr(0.28*x1 + 6.84*x2 >= 18)
    model.addConstr(6.33*x1 + 4.33*x2 >= 17)
    model.addConstr(5.0*x1 + 3.52*x2 >= 110)
    model.addConstr(-x1 + 6*x2 >= 0)
    model.addConstr(4.87*x1 + 1.72*x2 <= 92)
    model.addConstr(0.28*x1 + 6.84*x2 <= 58)
    model.addConstr(6.33*x1 + 4.33*x2 <= 37)
    model.addConstr(5.0*x1 + 3.52*x2 <= 231)

    # Optimize the model
    model.optimize()

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

solve_optimization_problem()
```