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 mathematical notation.

### Symbolic Representation

Let's define:
- $x_1$ as the hours worked by Ringo,
- $x_2$ as the hours worked by John.

The objective function to maximize is: $3.08x_1 + 8.47x_2$

Constraints:
1. Dollar cost per hour for Ringo: $12x_1$
2. Computer competence rating for Ringo: $23x_1$
3. Dollar cost per hour for John: $19x_2$
4. Computer competence rating for John: $12x_2$
5. Total combined dollar cost per hour: $12x_1 + 19x_2 \geq 11$
6. Total combined computer competence rating: $23x_1 + 12x_2 \geq 22$
7. Linear constraint: $-10x_1 + 6x_2 \geq 0$
8. Maximum total combined dollar cost per hour: $12x_1 + 19x_2 \leq 47$
9. Maximum total combined computer competence rating: $23x_1 + 12x_2 \leq 29$

Given the problem's requirements, we can represent it symbolically as:

```json
{
  'sym_variables': [('x1', 'hours worked by Ringo'), ('x2', 'hours worked by John')],
  'objective_function': '3.08*x1 + 8.47*x2',
  'constraints': [
    '12*x1 + 19*x2 >= 11',
    '23*x1 + 12*x2 >= 22',
    '-10*x1 + 6*x2 >= 0',
    '12*x1 + 19*x2 <= 47',
    '23*x1 + 12*x2 <= 29'
  ]
}
```

### Gurobi Code

To solve this optimization problem using Gurobi, we'll write the Python code as follows:

```python
from gurobipy import *

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

# Define variables
x1 = m.addVar(name="hours_worked_by_Ringo", lb=0)
x2 = m.addVar(name="hours_worked_by_John", lb=0)

# Set the objective function
m.setObjective(3.08*x1 + 8.47*x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(12*x1 + 19*x2 >= 11, name="min_total_dollar_cost")
m.addConstr(23*x1 + 12*x2 >= 22, name="min_total_competence")
m.addConstr(-10*x1 + 6*x2 >= 0, name="linear_constraint")
m.addConstr(12*x1 + 19*x2 <= 47, name="max_total_dollar_cost")
m.addConstr(23*x1 + 12*x2 <= 29, name="max_total_competence")

# Optimize the model
m.optimize()

# Print the solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Hours worked by Ringo: {x1.x}")
    print(f"Hours worked by John: {x2.x}")
    print(f"Objective function value: {m.objVal}")
else:
    print("No optimal solution found")
```