To solve the given optimization problem, we first need to translate the natural language description into a symbolic representation. This involves defining variables, an objective function, and constraints based on the provided information.

### Symbolic Representation

Let's define:
- $x_0$ as the hours worked by Dale,
- $x_1$ as the hours worked by Bobby.

The objective function to maximize is: $2.31x_0 + 9.7x_1$

Constraints are based on the given attributes and requirements:

1. Computer competence rating constraint: $4x_0 + 10x_1 \geq 7$
2. Organization score constraint: $11x_0 + 6x_1 \geq 20$
3. Work quality rating constraint: $11x_0 + 11x_1 \geq 9$
4. Additional linear constraint: $-x_0 + 8x_1 \geq 0$
5. Upper bound for computer competence rating: $4x_0 + 10x_1 \leq 16$
6. Upper bound for organization score: $11x_0 + 6x_1 \leq 51$
7. Upper bound for work quality rating: $11x_0 + 11x_1 \leq 35$

### Symbolic Representation in JSON Format

```json
{
    'sym_variables': [('x0', 'hours worked by Dale'), ('x1', 'hours worked by Bobby')],
    'objective_function': '2.31*x0 + 9.7*x1',
    'constraints': [
        '4*x0 + 10*x1 >= 7',
        '11*x0 + 6*x1 >= 20',
        '11*x0 + 11*x1 >= 9',
        '-x0 + 8*x1 >= 0',
        '4*x0 + 10*x1 <= 16',
        '11*x0 + 6*x1 <= 51',
        '11*x0 + 11*x1 <= 35'
    ]
}
```

### Gurobi Code

Now, let's implement this problem using Gurobi in Python:

```python
from gurobipy import *

# Create a model
m = Model("Optimization_Problem")

# Define variables
x0 = m.addVar(lb=0, name="hours_worked_by_Dale")
x1 = m.addVar(lb=0, name="hours_worked_by_Bobby")

# Set the objective function
m.setObjective(2.31*x0 + 9.7*x1, GRB.MAXIMIZE)

# Add constraints
m.addConstr(4*x0 + 10*x1 >= 7, "computer_competence_rating")
m.addConstr(11*x0 + 6*x1 >= 20, "organization_score")
m.addConstr(11*x0 + 11*x1 >= 9, "work_quality_rating")
m.addConstr(-x0 + 8*x1 >= 0, "additional_linear_constraint")
m.addConstr(4*x0 + 10*x1 <= 16, "computer_competence_upper_bound")
m.addConstr(11*x0 + 6*x1 <= 51, "organization_score_upper_bound")
m.addConstr(11*x0 + 11*x1 <= 35, "work_quality_rating_upper_bound")

# Optimize the model
m.optimize()

# Print the solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Hours worked by Dale: {x0.x}")
    print(f"Hours worked by Bobby: {x1.x}")
else:
    print("No optimal solution found")
```