Here's how we can formulate this linear programming problem and the corresponding Gurobi code:

**Decision Variables:**

* `x`: Amount invested in the film industry.
* `y`: Amount invested in the healthcare industry.

**Objective Function:**

Maximize the total return: `0.08x + 0.10y`

**Constraints:**

* **Total Investment:** `x + y <= 200000`
* **Minimum Film Investment:** `x >= 0.25 * 200000`  (25% of total investment)
* **Maximum Healthcare Investment:** `y <= 0.60 * 200000` (60% of total investment)
* **Non-negativity:** `x >= 0`, `y >= 0`


```python
import gurobipy as gp
from gurobipy import GRB

# Create a new model
m = gp.Model("investment_optimization")

# Create decision variables
x = m.addVar(lb=0, name="film_investment")
y = m.addVar(lb=0, name="healthcare_investment")

# Set objective function
m.setObjective(0.08 * x + 0.10 * y, GRB.MAXIMIZE)

# Add constraints
m.addConstr(x + y <= 200000, "total_investment")
m.addConstr(x >= 0.25 * 200000, "min_film_investment")
m.addConstr(y <= 0.60 * 200000, "max_healthcare_investment")

# Optimize the model
m.optimize()

# Print the results
if m.status == GRB.OPTIMAL:
    print(f"Optimal investment in Film Industry: ${x.x}")
    print(f"Optimal investment in Healthcare Industry: ${y.x}")
    print(f"Maximum return: ${m.objVal}")
elif m.status == GRB.INFEASIBLE:
    print("The model is infeasible.")
else:
    print(f"Optimization terminated with status {m.status}")

```
