To solve the given optimization problem, we first need to translate the natural language description into a symbolic representation. This involves defining the variables, the objective function, and the constraints using algebraic terms.

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

The objective function described is to minimize $7x_1 + 3x_2$.

The constraints given are:
1. The total combined dollar cost per hour from hours worked by Mary plus hours worked by Ringo has to be greater than or equal to 36: $17x_1 + 14x_2 \geq 36$.
2. The same as the first, implying no change in the formulation.
3. The total combined organization score from hours worked by Mary plus hours worked by Ringo has to be 35 at minimum: $8x_1 + 8x_2 \geq 35$.
4. The same as the third, indicating a repetition in constraints.
5. $-6x_1 + 5x_2 \geq 0$.
6. The total combined dollar cost per hour from hours worked by Mary and hours worked by Ringo must be no more than 60: $17x_1 + 14x_2 \leq 60$.
7. The total combined organization score from hours worked by Mary and hours worked by Ringo must be 93 at maximum: $8x_1 + 8x_2 \leq 93$.

Given the problem's description, we notice that constraints 1 and 2 are essentially the same, as are constraints 3 and 4. This means we can simplify our constraint list by removing the duplicates.

The symbolic representation of the problem is thus:
```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',
        '8*x1 + 8*x2 >= 35',
        '-6*x1 + 5*x2 >= 0',
        '17*x1 + 14*x2 <= 60',
        '8*x1 + 8*x2 <= 93'
    ]
}
```

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

```python
from gurobipy import *

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

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

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

# Add constraints
m.addConstr(17*x1 + 14*x2 >= 36, name="dollar_cost_constraint")
m.addConstr(8*x1 + 8*x2 >= 35, name="organization_score_min_constraint")
m.addConstr(-6*x1 + 5*x2 >= 0, name="mixed_hours_constraint")
m.addConstr(17*x1 + 14*x2 <= 60, name="dollar_cost_max_constraint")
m.addConstr(8*x1 + 8*x2 <= 93, name="organization_score_max_constraint")

# Optimize the model
m.optimize()

# Print the results
if m.status == GRB.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: {m.objVal}")
else:
    print("No optimal solution found")
```