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

## Step 2: Convert the natural language description into a symbolic representation
The symbolic representation of the variables is:
- $x_0$ : hours worked by Hank
- $x_1$ : hours worked by George
- $x_2$ : hours worked by Dale

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

## 4: Define the constraints in symbolic notation
The constraints are:
- $4.57x_0 \leq 115$ (not directly given but implied by the upper bound of paperwork competence rating for Hank, however, we need to consider the actual constraints provided)
- $4.99x_0 \leq 206$ (not directly given but implied by the upper bound of dollar cost per hour for Hank)
- $1.76x_1 \leq 115$
- $1.66x_1 \leq 206$
- $5.62x_2 \leq 115$
- $3.73x_2 \leq 206$
- $4.99x_0 + 1.66x_1 \geq 28$
- $4.99x_0 + 1.66x_1 + 3.73x_2 \geq 43$
- $1.76x_1 + 5.62x_2 \leq 64$
- $4.57x_0 + 5.62x_2 \leq 59$
- $4.57x_0 + 1.76x_1 + 5.62x_2 \leq 59$
- $4.99x_0 + 1.66x_1 \leq 124$
- $4.99x_0 + 1.66x_1 + 3.73x_2 \leq 124$

## 5: Create a dictionary for the symbolic variables and their corresponding natural language objects
The symbolic variables and their corresponding natural language objects are:
```json
{
    'sym_variables': [
        ('x0', 'hours worked by Hank'), 
        ('x1', 'hours worked by George'), 
        ('x2', 'hours worked by Dale')
    ], 
    'objective_function': '2*x0 + 2*x1 + 2*x2', 
    'constraints': [
        '4.99*x0 + 1.66*x1 >= 28', 
        '4.99*x0 + 1.66*x1 + 3.73*x2 >= 43', 
        '1.76*x1 + 5.62*x2 <= 64', 
        '4.57*x0 + 5.62*x2 <= 59', 
        '4.57*x0 + 1.76*x1 + 5.62*x2 <= 59', 
        '4.99*x0 + 1.66*x1 <= 124', 
        '4.99*x0 + 1.66*x1 + 3.73*x2 <= 124'
    ]
}
```

## 6: Write the Gurobi code to solve the optimization problem
```python
import gurobi

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

    # Define the variables
    x0 = model.addVar(name="x0", lb=0)  # hours worked by Hank
    x1 = model.addVar(name="x1", lb=0)  # hours worked by George
    x2 = model.addVar(name="x2", lb=0)  # hours worked by Dale

    # Define the objective function
    model.setObjective(2*x0 + 2*x1 + 2*x2, gurobi.GRB.MAXIMIZE)

    # Define the constraints
    model.addConstr(4.99*x0 + 1.66*x1 >= 28)
    model.addConstr(4.99*x0 + 1.66*x1 + 3.73*x2 >= 43)
    model.addConstr(1.76*x1 + 5.62*x2 <= 64)
    model.addConstr(4.57*x0 + 5.62*x2 <= 59)
    model.addConstr(4.57*x0 + 1.76*x1 + 5.62*x2 <= 59)
    model.addConstr(4.99*x0 + 1.66*x1 <= 124)
    model.addConstr(4.99*x0 + 1.66*x1 + 3.73*x2 <= 124)

    # Update the model
    model.update()

    # Solve the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print("Hours worked by Hank:", x0.varValue)
        print("Hours worked by George:", x1.varValue)
        print("Hours worked by Dale:", x2.varValue)
        print("Objective function value:", model.objVal)
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```