To solve the optimization problem described, we need to translate the given natural language description into a mathematical model that can be solved using Gurobi. The objective is to minimize the cost function subject to various constraints related to organization scores, work quality ratings, and dollar costs per hour.

### Model Formulation:

Let's denote:
- \(x_0\) as the hours worked by Peggy,
- \(x_1\) as the hours worked by Laura,
- \(x_2\) as the hours worked by Ringo.

The objective function to minimize is: 
\[9.55x_0 + 4.63x_1 + 1.21x_2\]

Constraints are based on the given information:
1. Organization score constraints:
   - \(8x_1 + 6x_2 \geq 26\)
   - \(2x_0 + 8x_1 \geq 18\)
   - \(2x_0 + 8x_1 + 6x_2 \geq 18\)
   - \(8x_1 + 6x_2 \leq 86\)
   - \(2x_0 + 6x_2 \leq 80\)

2. Work quality rating constraints:
   - \(8x_1 + 7x_2 \geq 10\)
   - \(6x_0 + 8x_1 + 7x_2 \geq 12\)
   - \(6x_0 + 7x_2 \leq 30\)

3. Dollar cost per hour constraints:
   - \(6x_0 + 10x_1 \geq 22\)
   - \(10x_1 + x_2 \geq 20\)
   - \(6x_0 + 10x_1 + x_2 \geq 20\)
   - \(6x_0 + 10x_1 \leq 78\)
   - \(6x_0 + x_2 \leq 95\)

4. Additional constraints:
   - \(-3x_0 + 9x_1 \geq 0\)

### Gurobi Model Implementation:

```python
from gurobipy import *

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

# Define variables
x0 = m.addVar(name='hours_worked_by_Peggy', lb=0, ub=GRB.INFINITY)
x1 = m.addVar(name='hours_worked_by_Laura', lb=0, ub=GRB.INFINITY)
x2 = m.addVar(name='hours_worked_by_Ringo', lb=0, ub=GRB.INFINITY)

# Objective function
m.setObjective(9.55*x0 + 4.63*x1 + 1.21*x2, GRB.MINIMIZE)

# Constraints
m.addConstr(8*x1 + 6*x2 >= 26, name='organization_score_Laura_Ringo_min')
m.addConstr(2*x0 + 8*x1 >= 18, name='organization_score_Peggy_Laura_min')
m.addConstr(2*x0 + 8*x1 + 6*x2 >= 18, name='organization_score_all_min')
m.addConstr(8*x1 + 6*x2 <= 86, name='organization_score_Laura_Ringo_max')
m.addConstr(2*x0 + 6*x2 <= 80, name='organization_score_Peggy_Ringo_max')

m.addConstr(8*x1 + 7*x2 >= 10, name='work_quality_Laura_Ringo_min')
m.addConstr(6*x0 + 8*x1 + 7*x2 >= 12, name='work_quality_all_min')
m.addConstr(6*x0 + 7*x2 <= 30, name='work_quality_Peggy_Ringo_max')

m.addConstr(6*x0 + 10*x1 >= 22, name='dollar_cost_Peggy_Laura_min')
m.addConstr(10*x1 + x2 >= 20, name='dollar_cost_Laura_Ringo_min')
m.addConstr(6*x0 + 10*x1 + x2 >= 20, name='dollar_cost_all_min')
m.addConstr(6*x0 + 10*x1 <= 78, name='dollar_cost_Peggy_Laura_max')
m.addConstr(6*x0 + x2 <= 95, name='dollar_cost_Peggy_Ringo_max')

m.addConstr(-3*x0 + 9*x1 >= 0, name='additional_constraint')

# Solve the model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Hours worked by Peggy: {x0.x}")
    print(f"Hours worked by Laura: {x1.x}")
    print(f"Hours worked by Ringo: {x2.x}")
else:
    print("No optimal solution found")
```