## Step 1: Define the symbolic representation of the variables
The variables are 'hours worked by Dale' 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 $6x_1 + 2x_2$.

## Step 3: Define the constraints in symbolic notation
The constraints given are:
- $2x_1 + 6x_2 \geq 6$ (total combined paperwork competence rating)
- $4x_1 + 9x_2 \geq 16$ (total combined computer competence rating)
- $-10x_1 + 6x_2 \geq 0$
- $2x_1 + 6x_2 \leq 27$ (total combined paperwork competence rating upper bound)
- $4x_1 + 9x_2 \leq 33$ (total combined computer competence rating upper bound)

## 4: Create a symbolic representation of the problem
The symbolic representation is:
```json
{
'sym_variables': [('x1', 'hours worked by Dale'), ('x2', 'hours worked by George')],
'objective_function': '6*x1 + 2*x2',
'constraints': [
    '2*x1 + 6*x2 >= 6',
    '4*x1 + 9*x2 >= 16',
    '-10*x1 + 6*x2 >= 0',
    '2*x1 + 6*x2 <= 27',
    '4*x1 + 9*x2 <= 33'
]
}
```

## 5: Implement the optimization problem using Gurobi
To solve this problem using Gurobi, we will use the Gurobi Python API.

```python
import gurobi

# Create a new model
model = gurobi.Model()

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

# Define the objective function
model.setObjective(6 * x1 + 2 * x2, gurobi.GRB.MAXIMIZE)

# Define the constraints
model.addConstr(2 * x1 + 6 * x2 >= 6, name="paperwork_rating")
model.addConstr(4 * x1 + 9 * x2 >= 16, name="computer_rating")
model.addConstr(-10 * x1 + 6 * x2 >= 0, name="hourly_constraint")
model.addConstr(2 * x1 + 6 * x2 <= 27, name="paperwork_upper_bound")
model.addConstr(4 * x1 + 9 * x2 <= 33, name="computer_upper_bound")

# Optimize the model
model.optimize()

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