## Step 1: Define the symbolic representation of the variables
The variables are 'hours worked by Mary' and 'hours worked by George', 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 $2.09x_1 + 2.23x_2$.

## Step 3: Define the constraints in symbolic notation
The constraints are:
1. $3x_1 + 10x_2 \geq 33$
2. $5x_1 - 6x_2 \geq 0$
3. $3x_1 + 10x_2 \leq 169$

## 4: Consider the bounds and integrality constraints
- $x_2$ must be an integer.
- The paperwork competence rating for Mary is 3, and for George is 10, but these are already incorporated into the constraints.
- The total combined paperwork competence rating has upper and lower bounds.

## 5: Express the problem in the required JSON format
```json
{
    'sym_variables': [('x1', 'hours worked by Mary'), ('x2', 'hours worked by George')],
    'objective_function': '2.09*x1 + 2.23*x2',
    'constraints': [
        '3*x1 + 10*x2 >= 33',
        '5*x1 - 6*x2 >= 0',
        '3*x1 + 10*x2 <= 169'
    ]
}
```

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

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

    # Define the variables
    x1 = model.addVar(name='hours_worked_by_Mary', lb=0)  # No upper bound for x1
    x2 = model.addVar(name='hours_worked_by_George', lb=0, vtype=gurobi.GRB.INTEGER)

    # Set the objective function
    model.setObjective(2.09 * x1 + 2.23 * x2, gurobi.GRB.MAXIMIZE)

    # Add constraints
    model.addConstr(3 * x1 + 10 * x2 >= 33)
    model.addConstr(5 * x1 - 6 * x2 >= 0)
    model.addConstr(3 * x1 + 10 * x2 <= 169)

    # Optimize the model
    model.optimize()

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

solve_optimization_problem()
```