Here's the formulation of the linear program and the Gurobi code to solve it:

**Decision Variables:**

* `x1`: Number of ads in grocery stores
* `x2`: Number of ads in train stations
* `x3`: Number of ads in water parks

**Objective Function:**

Maximize total viewership: `10000*x1 + 20000*x2 + 50000*x3`

**Constraints:**

* **Budget Constraint:** `300*x1 + 500*x2 + 1000*x3 <= 50000`
* **Train Station Limit:** `x2 <= 15`
* **Water Park Proportion:** `x3 <= (x1 + x2 + x3)/3`  (At most one-third of total ads)
* **Grocery Store Minimum:** `x1 >= 0.1*(x1 + x2 + x3)` (At least 10% of total ads)
* **Non-negativity:** `x1, x2, x3 >= 0`


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

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

# Create variables
x1 = model.addVar(lb=0, vtype=GRB.INTEGER, name="grocery_ads")
x2 = model.addVar(lb=0, vtype=GRB.INTEGER, name="train_ads")
x3 = model.addVar(lb=0, vtype=GRB.INTEGER, name="waterpark_ads")

# Set objective function
model.setObjective(10000*x1 + 20000*x2 + 50000*x3, GRB.MAXIMIZE)

# Add constraints
model.addConstr(300*x1 + 500*x2 + 1000*x3 <= 50000, "budget")
model.addConstr(x2 <= 15, "train_limit")
model.addConstr(x3 <= (x1 + x2 + x3)/3, "waterpark_proportion")
model.addConstr(x1 >= 0.1*(x1 + x2 + x3), "grocery_minimum")


# Optimize model
model.optimize()

# Print results
if model.status == GRB.OPTIMAL:
    print(f"Optimal Viewership: {model.objVal}")
    print(f"Grocery Store Ads: {x1.x}")
    print(f"Train Station Ads: {x2.x}")
    print(f"Water Park Ads: {x3.x}")
elif model.status == GRB.INFEASIBLE:
    print("The model is infeasible.")
else:
    print(f"Optimization terminated with status {model.status}")

```
