## Step 1: Define the symbolic variables
Let's define the symbolic variables as follows:
- $x_1$ represents the number of ads on billboards
- $x_2$ represents the number of ads on podcasts
- $x_3$ represents the number of ads on merchandises

## Step 2: Convert the natural language description into a symbolic representation
The objective is to maximize viewership. The viewership for each type of ad is given as:
- Billboards: 40000 viewers per ad
- Podcasts: 10000 viewers per ad
- Merchandises: 2000 viewers per ad

So, the objective function can be represented as: $40000x_1 + 10000x_2 + 2000x_3$

## 3: Define the constraints
The constraints are:
- The cost for an ad on each:
  - Billboards: $750
  - Podcasts: $1000
  - Merchandises: $300
- The total budget is $20000: $750x_1 + 1000x_2 + 300x_3 \leq 20000$
- The billboard provider limits the number of ads from the same company to 3: $x_1 \leq 3$
- At most 40% of the total number of ads can occur on merchandises: $x_3 \leq 0.4(x_1 + x_2 + x_3)$
- At least 25% of the total number of ads should occur on podcasts: $x_2 \geq 0.25(x_1 + x_2 + x_3)$
- Non-negativity constraints: $x_1, x_2, x_3 \geq 0$ and are integers.

## 4: Simplify the constraints
- $x_3 \leq 0.4(x_1 + x_2 + x_3)$ simplifies to $0.6x_3 \leq 0.4x_1 + 0.4x_2$ or $1.5x_3 \leq x_1 + x_2$
- $x_2 \geq 0.25(x_1 + x_2 + x_3)$ simplifies to $0.75x_2 \geq 0.25x_1 + 0.25x_3$ or $3x_2 \geq x_1 + x_3$

## 5: Symbolic representation in JSON format
```json
{
    'sym_variables': [('x1', 'ads on billboards'), ('x2', 'ads on podcasts'), ('x3', 'ads on merchandises')],
    'objective_function': '40000*x1 + 10000*x2 + 2000*x3',
    'constraints': [
        '750*x1 + 1000*x2 + 300*x3 <= 20000',
        'x1 <= 3',
        '1.5*x3 <= x1 + x2',
        '3*x2 >= x1 + x3',
        'x1, x2, x3 >= 0 and are integers'
    ]
}
```

## 6: Gurobi code in Python
```python
import gurobi

def solve_maximus_ads():
    # Create a new model
    model = gurobi.Model()

    # Define the variables
    x1 = model.addVar(name="billboards", vtype=gurobi.GRB.INTEGER, lb=0)
    x2 = model.addVar(name="podcasts", vtype=gurobi.GRB.INTEGER, lb=0)
    x3 = model.addVar(name="merchandises", vtype=gurobi.GRB.INTEGER, lb=0)

    # Objective function
    model.setObjective(40000*x1 + 10000*x2 + 2000*x3, gurobi.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(750*x1 + 1000*x2 + 300*x3 <= 20000, name="budget_constraint")
    model.addConstr(x1 <= 3, name="billboard_limit")
    model.addConstr(1.5*x3 <= x1 + x2, name="merchandise_limit")
    model.addConstr(3*x2 >= x1 + x3, name="podcast_requirement")

    # Solve the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Ads on billboards: {x1.varValue}")
        print(f"Ads on podcasts: {x2.varValue}")
        print(f"Ads on merchandises: {x3.varValue}")
        print(f"Max viewership: {40000*x1.varValue + 10000*x2.varValue + 2000*x3.varValue}")
    else:
        print("No optimal solution found.")

solve_maximus_ads()
```