To solve the optimization problem described, we'll translate the given natural language description into a mathematical formulation and then implement it using Gurobi in Python. The objective is to maximize the total value of work done by Paul, Hank, and Mary, subject to several constraints related to their work quality ratings and the total hours worked.

Given:
- Objective function: Maximize \(6.35 \times \text{hours worked by Paul} + 3.95 \times \text{hours worked by Hank} + 9.82 \times \text{hours worked by Mary}\)
- Constraints:
  - Work quality ratings: Paul (0.95), Hank (0.55), Mary (0.72)
  - Minimum combined work quality rating for Paul and Hank: 24
  - Minimum combined work quality rating for all three: 16
  - Maximum combined work quality rating for Paul and Mary: 61
  - Maximum combined work quality rating for all three: 61
  - Hours worked by each must be a non-fractional (integer) amount.

We will use Gurobi's Python interface to model and solve this problem. The decision variables are the hours worked by Paul, Hank, and Mary. We'll denote these as \(P\), \(H\), and \(M\) respectively.

```python
from gurobipy import *

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

# Decision variables: Hours worked by each person
P = m.addVar(vtype=GRB.INTEGER, name="Hours_Paul")
H = m.addVar(vtype=GRB.INTEGER, name="Hours_Hank")
M = m.addVar(vtype=GRB.INTEGER, name="Hours_Mary")

# Objective function: Maximize total value of work
m.setObjective(6.35*P + 3.95*H + 9.82*M, GRB.MAXIMIZE)

# Constraints
# Minimum combined work quality rating for Paul and Hank
m.addConstr(0.95*P + 0.55*H >= 24, name="Min_Quality_PH")

# Minimum combined work quality rating for all three
m.addConstr(0.95*P + 0.55*H + 0.72*M >= 16, name="Min_Quality_PHM")

# Maximum combined work quality rating for Paul and Mary
m.addConstr(0.95*P + 0.72*M <= 61, name="Max_Quality_PM")

# Maximum combined work quality rating for all three
m.addConstr(0.95*P + 0.55*H + 0.72*M <= 61, name="Max_Quality_PHM")

# Optimize the model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Hours worked by Paul: {P.x}")
    print(f"Hours worked by Hank: {H.x}")
    print(f"Hours worked by Mary: {M.x}")
    print(f"Objective function value: {m.objVal}")
else:
    print("No optimal solution found")
```
```python
```