To solve the given optimization problem using Gurobi, we first need to understand the variables and constraints involved. The problem aims to minimize an objective function that depends on the hours worked by Jean and Mary, subject to various constraints related to their organization scores, dollar costs per hour, computer competence ratings, and paperwork competence ratings.

The variables in this problem are:
- `x0`: Hours worked by Jean
- `x1`: Hours worked by Mary

Given information:
- Objective function: Minimize `5.11*x0 + 6.35*x1`
- Constraints:
  - Organization score: `5*x0 + 1*x1 >= 19` and `5*x0 + 1*x1 <= 34`
  - Dollar cost per hour: `12*x0 + 4*x1 >= 18` and `12*x0 + 4*x1 <= 31`
  - Computer competence rating: `10*x0 + 7*x1 >= 21` and `10*x0 + 7*x1 <= 52`
  - Paperwork competence rating: `12*x0 + 14*x1 >= 10` and `12*x0 + 14*x1 <= 42`
  - Additional constraint: `8*x0 - x1 >= 0`

Since both Jean's and Mary's hours can be fractional, we don't need to specify them as integers in the model.

Here is how you could implement this problem using Gurobi in Python:

```python
from gurobipy import *

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

# Define variables
x0 = m.addVar(name="hours_worked_by_Jean", lb=0)  # Hours worked by Jean
x1 = m.addVar(name="hours_worked_by_Mary", lb=0)  # Hours worked by Mary

# Set the objective function
m.setObjective(5.11*x0 + 6.35*x1, GRB.MINIMIZE)

# Add constraints
m.addConstr(5*x0 + 1*x1 >= 19, name="organization_score_min")
m.addConstr(5*x0 + 1*x1 <= 34, name="organization_score_max")
m.addConstr(12*x0 + 4*x1 >= 18, name="dollar_cost_per_hour_min")
m.addConstr(12*x0 + 4*x1 <= 31, name="dollar_cost_per_hour_max")
m.addConstr(10*x0 + 7*x1 >= 21, name="computer_competence_rating_min")
m.addConstr(10*x0 + 7*x1 <= 52, name="computer_competence_rating_max")
m.addConstr(12*x0 + 14*x1 >= 10, name="paperwork_competence_rating_min")
m.addConstr(12*x0 + 14*x1 <= 42, name="paperwork_competence_rating_max")
m.addConstr(8*x0 - x1 >= 0, name="additional_constraint")

# Optimize the model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Hours worked by Jean: {x0.x}")
    print(f"Hours worked by Mary: {x1.x}")
    print(f"Objective function value: {m.objVal}")
else:
    print("No optimal solution found")
```