To solve this optimization problem, we first need to define the decision variables and the objective function. Let's denote:

- \(x_f\) as the amount invested in the film industry.
- \(x_h\) as the amount invested in the healthcare industry.

The city has $200,000 available for investment. Thus, the total investment is constrained by \(x_f + x_h = 200,000\).

The return on investment (ROI) from the film industry is 8%, and from the healthcare industry is 10%. Therefore, the total return can be calculated as \(0.08x_f + 0.10x_h\), which we aim to maximize.

Given that the city wants to invest at least 25% of its funds in the film industry, we have \(x_f \geq 0.25 \times 200,000 = 50,000\). Similarly, since it wants to invest no more than 60% in the healthcare industry, we have \(x_h \leq 0.60 \times 200,000 = 120,000\).

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

```python
from gurobipy import *

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

# Define the decision variables
x_f = m.addVar(name="film_investment", lb=0)
x_h = m.addVar(name="healthcare_investment", lb=0)

# Objective function: Maximize return on investment
m.setObjective(0.08*x_f + 0.10*x_h, GRB.MAXIMIZE)

# Constraints
m.addConstr(x_f + x_h == 200000)  # Total investment constraint
m.addConstr(x_f >= 50000)         # Minimum investment in film industry
m.addConstr(x_h <= 120000)        # Maximum investment in healthcare industry

# Optimize the model
m.optimize()

# Print the results
if m.status == GRB.OPTIMAL:
    print(f"Optimal film investment: ${x_f.x:.2f}")
    print(f"Optimal healthcare investment: ${x_h.x:.2f}")
    print(f"Maximum return on investment: ${m.ObjVal:.2f}")
else:
    print("Model is infeasible")
```