## Step 1: Define the symbolic representation of the problem
The problem involves two variables: 'hours worked by Ringo' and 'hours worked by Bobby'. Let's denote 'hours worked by Ringo' as $x_1$ and 'hours worked by Bobby' as $x_2$. The objective function to minimize is $3.18x_1 + 2.93x_2$.

## Step 2: List the constraints in symbolic notation
The constraints given are:
1. $9x_1 + 10x_2 \geq 53$
2. $2x_1 + 5x_2 \geq 27$
3. $-6x_1 + 10x_2 \geq 0$
4. $9x_1 + 10x_2 \leq 110$
5. $2x_1 + 5x_2 \leq 73$
And the non-negativity constraints are implicitly handled by allowing $x_1$ and $x_2$ to be fractional.

## 3: Define the symbolic variables and objective function
The symbolic variables are:
- $x_1$ for 'hours worked by Ringo'
- $x_2$ for 'hours worked by Bobby'

The objective function is $3.18x_1 + 2.93x_2$.

## 4: Express the problem in the required 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'
    ]
}
```

## 5: Implement the problem using Gurobi
```python
import gurobi

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

    # Define the variables
    x1 = model.addVar(name='hours_worked_by_Ringo', lb=0)  # Assuming non-negativity
    x2 = model.addVar(name='hours_worked_by_Bobby', lb=0)  # Assuming non-negativity

    # Define the objective function
    model.setObjective(3.18 * x1 + 2.93 * x2, gurobi.GRB.MINIMIZE)

    # Add constraints
    model.addConstr(9 * x1 + 10 * x2 >= 53)
    model.addConstr(2 * x1 + 5 * x2 >= 27)
    model.addConstr(-6 * x1 + 10 * x2 >= 0)
    model.addConstr(9 * x1 + 10 * x2 <= 110)
    model.addConstr(2 * x1 + 5 * x2 <= 73)

    # Optimize the model
    model.optimize()

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

solve_optimization_problem()
```