To solve the given optimization problem, 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 problem statement.

Let's define:
- $x_1$ as the number of LCD monitors made each day.
- $x_2$ as the number of LED monitors made each day.

The objective function, which represents the total profit, can be written as:
\[25x_1 + 70x_2\]

We aim to maximize this function.

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 monitor production requirement: $x_1 + x_2 \geq 250$

Thus, the symbolic representation of our problem is:
```json
{
    'sym_variables': [('x1', 'number of LCD monitors'), ('x2', 'number of LED monitors')],
    'objective_function': '25*x1 + 70*x2',
    'constraints': ['x1 >= 150', 'x2 >= 80', 'x1 <= 300', 'x2 <= 280', 'x1 + x2 >= 250']
}
```

To solve this linear programming problem using Gurobi in Python, we will use the following code:

```python
from gurobipy import *

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

# Define variables
x1 = m.addVar(vtype=GRB.CONTINUOUS, name="LCD_Monitors")
x2 = m.addVar(vtype=GRB.CONTINUOUS, name="LED_Monitors")

# Set the objective function: Maximize profit
m.setObjective(25*x1 + 70*x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(x1 >= 150, "LCD_Demand")
m.addConstr(x2 >= 80, "LED_Demand")
m.addConstr(x1 <= 300, "LCD_Production_Limit")
m.addConstr(x2 <= 280, "LED_Production_Limit")
m.addConstr(x1 + x2 >= 250, "Total_Monitors_Requirement")

# Optimize the model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"LCD Monitors: {x1.x}")
    print(f"LED Monitors: {x2.x}")
    print(f"Total Profit: ${25*x1.x + 70*x2.x:.2f}")
else:
    print("No optimal solution found")
```