## Step 1: Define the symbolic representation of the problem
The problem involves two variables: 'hours worked by Dale' and 'hours worked by Bobby'. Let's denote 'hours worked by Dale' as $x_1$ and 'hours worked by Bobby' as $x_2$. The objective function to maximize is $2.31x_1 + 9.7x_2$.

## Step 2: List the constraints in symbolic notation
The constraints given are:
1. $4x_1 + 10x_2 \geq 7$ (computer competence rating)
2. $11x_1 + 6x_2 \geq 20$ (organization score)
3. $11x_1 + 11x_2 \geq 9$ (work quality rating)
4. $-x_1 + 8x_2 \geq 0$
5. $4x_1 + 10x_2 \leq 16$ (computer competence rating upper bound)
6. $11x_1 + 6x_2 \leq 51$ (organization score upper bound)
7. $11x_1 + 11x_2 \leq 35$ (work quality rating upper bound)
8. $x_1 \geq 0$ and $x_2 \geq 0$ (non-negativity, implicitly given as hours cannot be negative)

## 3: Correct and Simplify Constraints
Upon review, constraints 5, 6, and 7 already imply upper bounds, and there are duplicate constraints (e.g., 5 and another similar one). The actual constraints to consider are:
- $4x_1 + 10x_2 \leq 25$ (corrected upper bound for computer competence)
- $11x_1 + 6x_2 \leq 56$ (corrected upper bound for organization score)
- $11x_1 + 11x_2 \leq 49$ (corrected upper bound for work quality rating)

## 4: Symbolic Representation
The symbolic representation is:
```json
{
'sym_variables': [('x1', 'hours worked by Dale'), ('x2', 'hours worked by Bobby')],
'objective_function': '2.31*x1 + 9.7*x2',
'constraints': [
    '4*x1 + 10*x2 >= 7',
    '11*x1 + 6*x2 >= 20',
    '11*x1 + 11*x2 >= 9',
    '-1*x1 + 8*x2 >= 0',
    '4*x1 + 10*x2 <= 25',
    '11*x1 + 6*x2 <= 56',
    '11*x1 + 11*x2 <= 49'
]
}
```

## 5: Gurobi Code
Now, let's write the Gurobi code for this problem:
```python
import gurobi

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

    # Define variables
    x1 = model.addVar(name="hours_worked_by_Dale", lb=0)
    x2 = model.addVar(name="hours_worked_by_Bobby", lb=0)

    # Objective function
    model.setObjective(2.31 * x1 + 9.7 * x2, gurobi.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(4 * x1 + 10 * x2 >= 7, name="computer_competence_rating")
    model.addConstr(11 * x1 + 6 * x2 >= 20, name="organization_score")
    model.addConstr(11 * x1 + 11 * x2 >= 9, name="work_quality_rating")
    model.addConstr(-x1 + 8 * x2 >= 0, name="dale_bobby_hours")
    model.addConstr(4 * x1 + 10 * x2 <= 25, name="computer_competence_upper_bound")
    model.addConstr(11 * x1 + 6 * x2 <= 56, name="organization_upper_bound")
    model.addConstr(11 * x1 + 11 * x2 <= 49, name="work_quality_upper_bound")

    # Optimize
    model.optimize()

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

solve_optimization_problem()
```