## Step 1: Define the symbolic representation of the variables
The variables are 'hours worked by Mary' and 'hours worked by Ringo', which can be represented symbolically as $x_1$ and $x_2$ respectively.

## Step 2: Define the objective function in symbolic notation
The objective function to minimize is $7x_1 + 3x_2$.

## Step 3: Define the constraints in symbolic notation
The constraints are:
- $17x_1 + 14x_2 \geq 36$
- $17x_1 + 14x_2 \leq 60$ (implied by the previous and the specific upper bound of 60)
- $8x_1 + 8x_2 \geq 35$
- $8x_1 + 8x_2 \leq 93$
- $-6x_1 + 5x_2 \geq 0$

## 4: Correct and Simplify Constraints
Correcting and simplifying the constraints based on the problem statement:
- The total combined dollar cost per hour must be at least 36: $17x_1 + 14x_2 \geq 36$
- The total combined dollar cost per hour must be no more than 60: $17x_1 + 14x_2 \leq 60$
- The total combined organization score must be at least 35: $8x_1 + 8x_2 \geq 35$
- The total combined organization score must be no more than 93: $8x_1 + 8x_2 \leq 93$
- The constraint $-6x_1 + 5x_2 \geq 0$ remains as is.

## 5: Create the symbolic representation
The symbolic representation of the problem is:
```json
{
'sym_variables': [('x1', 'hours worked by Mary'), ('x2', 'hours worked by Ringo')],
'objective_function': '7*x1 + 3*x2',
'constraints': [
    '17*x1 + 14*x2 >= 36',
    '17*x1 + 14*x2 <= 60',
    '8*x1 + 8*x2 >= 35',
    '8*x1 + 8*x2 <= 93',
    '-6*x1 + 5*x2 >= 0'
]
}
```

## 6: Implement the problem in Gurobi
Now, let's implement this problem in Gurobi using Python.

```python
import gurobi

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

    # Define the variables
    x1 = model.addVar(name="hours_worked_by_Mary", lb=0)  # Assuming non-negative hours
    x2 = model.addVar(name="hours_worked_by_Ringo", lb=0)  # Assuming non-negative hours

    # Set the objective function
    model.setObjective(7 * x1 + 3 * x2, gurobi.GRB.MINIMIZE)

    # Add constraints
    model.addConstr(17 * x1 + 14 * x2 >= 36)
    model.addConstr(17 * x1 + 14 * x2 <= 60)
    model.addConstr(8 * x1 + 8 * x2 >= 35)
    model.addConstr(8 * x1 + 8 * x2 <= 93)
    model.addConstr(-6 * x1 + 5 * x2 >= 0)

    # Optimize the model
    model.optimize()

    # Print the status
    if model.status == gurobi.GRB.Status.OPTIMAL:
        print("Optimal solution found.")
        print(f"Hours worked by Mary: {x1.x}")
        print(f"Hours worked by Ringo: {x2.x}")
        print(f"Objective function value: {model.objVal}")
    elif model.status == gurobi.GRB.Status.INFEASIBLE:
        print("No feasible solution exists.")
    else:
        print("No optimal solution could be found.")

solve_optimization_problem()
```