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 using algebraic terms.

Let's denote:
- $x_0$ as the hours worked by John,
- $x_1$ as the hours worked by George.

The objective function is given as: minimize $3x_0 + x_1$.

Constraints are as follows:
1. Productivity rating constraint: $14x_0 + 17x_1 \geq 39$
2. Computer competence rating constraint: $14x_0 + 4x_1 \geq 21$
3. Organization score constraint: $11x_0 + 20x_1 \geq 66$
4. Additional linear constraint: $2x_0 - 7x_1 \geq 0$
5. Upper bound productivity rating constraint: $14x_0 + 17x_1 \leq 106$
6. Upper bound computer competence rating constraint: $14x_0 + 4x_1 \leq 55$
7. Upper bound organization score constraint: $11x_0 + 20x_1 \leq 131$

Given these definitions, the symbolic representation of the problem is:

```json
{
    'sym_variables': [('x0', 'hours worked by John'), ('x1', 'hours worked by George')],
    'objective_function': 'minimize 3*x0 + x1',
    'constraints': [
        '14*x0 + 17*x1 >= 39',
        '14*x0 + 4*x1 >= 21',
        '11*x0 + 20*x1 >= 66',
        '2*x0 - 7*x1 >= 0',
        '14*x0 + 17*x1 <= 106',
        '14*x0 + 4*x1 <= 55',
        '11*x0 + 20*x1 <= 131'
    ]
}
```

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

```python
from gurobipy import *

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

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

# Set the objective function
m.setObjective(3*x0 + x1, GRB.MINIMIZE)

# Add constraints
m.addConstr(14*x0 + 17*x1 >= 39, "productivity_rating_constraint")
m.addConstr(14*x0 + 4*x1 >= 21, "computer_competence_rating_constraint")
m.addConstr(11*x0 + 20*x1 >= 66, "organization_score_constraint")
m.addConstr(2*x0 - 7*x1 >= 0, "additional_linear_constraint")
m.addConstr(14*x0 + 17*x1 <= 106, "upper_bound_productivity_rating_constraint")
m.addConstr(14*x0 + 4*x1 <= 55, "upper_bound_computer_competence_rating_constraint")
m.addConstr(11*x0 + 20*x1 <= 131, "upper_bound_organization_score_constraint")

# Optimize the model
m.optimize()

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