## Symbolic Representation

To solve the given optimization problem, we first need to convert the natural language description into a symbolic representation.

Let's define the symbolic variables as follows:

- $x_1$ represents the number of LCD monitors
- $x_2$ represents the number of LED monitors

The objective is to maximize profit. The profit per LCD monitor is $25, and the profit per LED monitor is $70. Therefore, the objective function can be represented as:

Maximize: $25x_1 + 70x_2$

The constraints based on the problem description are:

1. Demand for LCD monitors: $x_1 \geq 150$
2. Demand for LED monitors: $x_2 \geq 80$
3. Production limit for LCD monitors: $x_1 \leq 300$
4. Production limit for LED monitors: $x_2 \leq 280$
5. Total monitors contract: $x_1 + x_2 \geq 250$

## Symbolic Representation in JSON Format

```json
{
    'sym_variables': [('x1', 'LCD monitors'), ('x2', 'LED monitors')],
    'objective_function': '25*x1 + 70*x2',
    'constraints': [
        'x1 >= 150',
        'x2 >= 80',
        'x1 <= 300',
        'x2 <= 280',
        'x1 + x2 >= 250'
    ]
}
```

## Gurobi Code in Python

```python
import gurobipy as gp

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

# Define the variables
x1 = model.addVar(lb=0, name="LCD_monitors")  # Number of LCD monitors
x2 = model.addVar(lb=0, name="LED_monitors")  # Number of LED monitors

# Objective function: Maximize profit
model.setObjective(25*x1 + 70*x2, gp.GRB.MAXIMIZE)

# Constraints
model.addConstr(x1 >= 150, name="LCD_demand")
model.addConstr(x2 >= 80, name="LED_demand")
model.addConstr(x1 <= 300, name="LCD_production_limit")
model.addConstr(x2 <= 280, name="LED_production_limit")
model.addConstr(x1 + x2 >= 250, name="total_monitors_contract")

# Solve the model
model.optimize()

# Print the solution
if model.status == gp.GRB.OPTIMAL:
    print("Optimal Solution:")
    print(f"LCD monitors: {x1.varValue}")
    print(f"LED monitors: {x2.varValue}")
    print(f"Max Profit: {model.objVal}")
else:
    print("No optimal solution found.")
```