To solve this optimization problem, we first need to define the decision variables and the objective function. Let's denote:

- \(x\) as the number of acres of corn to be grown.
- \(y\) as the number of acres of peas to be grown.

The objective is to maximize profit. Given that each acre of corn yields a profit of $200 and each acre of peas yields a profit of $250, our objective function can be written as:

\[ \text{Maximize:} \quad 200x + 250y \]

Now, let's define the constraints based on the given information:

1. **Fertilizer Cost Constraint**: The total cost for fertilizer must not exceed $4350.
   - Each acre of corn requires $50 worth of fertilizer.
   - Each acre of peas requires $60 worth of fertilizer.
   - Therefore, the constraint is: \(50x + 60y \leq 4350\).

2. **Time Constraint**: The total time to lay fertilizer must not exceed 6000 minutes.
   - Each acre of corn requires 60 minutes.
   - Each acre of peas requires 90 minutes.
   - Therefore, the constraint is: \(60x + 90y \leq 6000\).

3. **Non-Negativity Constraints**: The number of acres for each crop cannot be negative.
   - \(x \geq 0\)
   - \(y \geq 0\)

4. **Land Constraint**: The total land used cannot exceed 40 acres.
   - \(x + y \leq 40\)

Now, let's translate these constraints and the objective function into Gurobi code in Python:

```python
from gurobipy import *

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

# Define variables
x = m.addVar(name="corn_acres", lb=0)  # Acres of corn
y = m.addVar(name="peas_acres", lb=0)  # Acres of peas

# Set the objective function: Maximize profit
m.setObjective(200*x + 250*y, GRB.MAXIMIZE)

# Add constraints
m.addConstr(50*x + 60*y <= 4350, name="fertilizer_cost_constraint")
m.addConstr(60*x + 90*y <= 6000, name="time_constraint")
m.addConstr(x + y <= 40, name="land_constraint")

# Optimize the model
m.optimize()

# Print results
if m.status == GRB.OPTIMAL:
    print(f"Optimal solution found: {x.varName} = {x.x}, {y.varName} = {y.x}")
    print(f"Maximum Profit: ${200*x.x + 250*y.x:.2f}")
else:
    print("No optimal solution found")
```