To solve the optimization problem described, we first need to establish a clear symbolic representation of the variables and constraints involved. The problem involves minimizing an objective function subject to several linear constraints.

### Symbolic Representation:

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

The objective function is to minimize: $3.18x_1 + 2.93x_2$.

Constraints are:
1. $9x_1 + 10x_2 \geq 53$ (Total combined computer competence rating should be 53 or more),
2. $9x_1 + 10x_2 \geq 53$ (Duplicate constraint, same as above),
3. $2x_1 + 5x_2 \geq 27$ (Total combined paperwork competence rating should be 27 at a minimum),
4. $2x_1 + 5x_2 \geq 27$ (Duplicate constraint, same as above),
5. $-6x_1 + 10x_2 \geq 0$,
6. $9x_1 + 10x_2 \leq 110$ (Total combined computer competence rating should not exceed 110),
7. $2x_1 + 5x_2 \leq 73$ (Total combined paperwork competence rating must not exceed 73).

### Symbolic Representation in JSON Format:
```json
{
    'sym_variables': [('x1', 'hours worked by Ringo'), ('x2', 'hours worked by Bobby')],
    'objective_function': '3.18*x1 + 2.93*x2',
    'constraints': [
        '9*x1 + 10*x2 >= 53',
        '2*x1 + 5*x2 >= 27',
        '-6*x1 + 10*x2 >= 0',
        '9*x1 + 10*x2 <= 110',
        '2*x1 + 5*x2 <= 73'
    ]
}
```

### Gurobi Code:
```python
from gurobipy import *

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

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

# Set the objective function
m.setObjective(3.18*x1 + 2.93*x2, GRB.MINIMIZE)

# Add constraints
m.addConstr(9*x1 + 10*x2 >= 53, "computer_competence_rating")
m.addConstr(2*x1 + 5*x2 >= 27, "paperwork_competence_rating")
m.addConstr(-6*x1 + 10*x2 >= 0, "additional_constraint")
m.addConstr(9*x1 + 10*x2 <= 110, "max_computer_competence")
m.addConstr(2*x1 + 5*x2 <= 73, "max_paperwork_competence")

# Optimize the model
m.optimize()

# Print results
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Hours worked by Ringo: {x1.x}")
    print(f"Hours worked by Bobby: {x2.x}")
else:
    print("No optimal solution found")
```