To solve this problem, we first need to define the symbolic representation of the variables and the objective function, followed by the constraints. 

The given variables are 'hours worked by Ringo' and 'hours worked by George'. Let's denote these as `x1` and `x2`, respectively.

The objective function is to minimize `1.21*x1 + 9.67*x2`.

Now, let's list out the constraints:

1. The total combined dollar cost per hour from hours worked by Ringo plus hours worked by George should be equal to or greater than 59: `25*x1 + 18*x2 >= 59`.
2. The same constraint is repeated, so we ignore it.
3. The total combined paperwork competence rating from hours worked by Ringo plus hours worked by George has to be 38 at a minimum: `24*x1 + 5*x2 >= 38`.
4. This constraint is the same as the previous one, so we ignore it.
5. Seven times the number of hours worked by Ringo, plus -1 times the number of hours worked by George should be at least zero: `7*x1 - x2 >= 0`.
6. The total combined dollar cost per hour from hours worked by Ringo plus hours worked by George must be 87 at a maximum: `25*x1 + 18*x2 <= 87`.
7. The total combined paperwork competence rating from hours worked by Ringo and hours worked by George should be 149 or less: `24*x1 + 5*x2 <= 149`.

Here is the symbolic representation of the problem in JSON format:
```json
{
    'sym_variables': [('x1', 'hours worked by Ringo'), ('x2', 'hours worked by George')],
    'objective_function': 'minimize 1.21*x1 + 9.67*x2',
    'constraints': [
        '25*x1 + 18*x2 >= 59',
        '24*x1 + 5*x2 >= 38',
        '7*x1 - x2 >= 0',
        '25*x1 + 18*x2 <= 87',
        '24*x1 + 5*x2 <= 149'
    ]
}
```

Now, let's write the Gurobi code to solve this problem:
```python
from gurobipy import *

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

# Define variables
x1 = m.addVar(lb=0, ub=GRB.INFINITY, vtype=GRB.CONTINUOUS, name="hours_worked_by_Ringo")
x2 = m.addVar(lb=0, ub=GRB.INFINITY, vtype=GRB.CONTINUOUS, name="hours_worked_by_George")

# Define objective function
m.setObjective(1.21*x1 + 9.67*x2, GRB.MINIMIZE)

# Add constraints
m.addConstr(25*x1 + 18*x2 >= 59, "dollar_cost_constraint")
m.addConstr(24*x1 + 5*x2 >= 38, "paperwork_competence_constraint")
m.addConstr(7*x1 - x2 >= 0, "ringo_george_hours_constraint")
m.addConstr(25*x1 + 18*x2 <= 87, "max_dollar_cost_constraint")
m.addConstr(24*x1 + 5*x2 <= 149, "max_paperwork_competence_constraint")

# Optimize model
m.optimize()

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