To solve the optimization problem described, we first need to translate the natural language description into a symbolic representation. This involves defining variables, an objective function, and constraints based on the given information.

Let's define:
- $x_1$ as the number of newspaper advertisements.
- $x_2$ as the number of television advertisements.

The objective is to maximize exposure, which can be represented by the function:
\[ \text{Maximize} \quad 30000x_1 + 50000x_2 \]

Given constraints are:
1. Budget constraint: $2500x_1 + 5000x_2 \leq 200000$
2. Lower bound for newspaper advertisements: $x_1 \geq 12$
3. Upper bound for newspaper advertisements: $x_1 \leq 24$
4. Lower bound for television advertisements: $x_2 \geq 10$

Thus, the symbolic representation of the problem is:
```json
{
  'sym_variables': [('x1', 'number of newspaper advertisements'), ('x2', 'number of television advertisements')],
  'objective_function': '30000*x1 + 50000*x2',
  'constraints': [
    '2500*x1 + 5000*x2 <= 200000',
    'x1 >= 12',
    'x1 <= 24',
    'x2 >= 10'
  ]
}
```

To solve this problem using Gurobi in Python, we'll write the following code:

```python
from gurobipy import *

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

# Define variables
x1 = m.addVar(lb=12, ub=24, vtype=GRB.INTEGER, name="newspaper_advertisements")
x2 = m.addVar(lb=10, vtype=GRB.INTEGER, name="television_advertisements")

# Set the objective function
m.setObjective(30000*x1 + 50000*x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(2500*x1 + 5000*x2 <= 200000, "budget_constraint")

# Optimize model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Number of newspaper advertisements: {x1.x}")
    print(f"Number of television advertisements: {x2.x}")
    print(f"Maximum exposure: {m.objVal}")
else:
    print("No optimal solution found")
```