Here's the formulation of the linear program and the Gurobi code to solve it:

**Decision Variables:**

* `x`: Acres of corn planted
* `y`: Acres of peas planted

**Objective Function:**

Maximize profit: `200x + 250y`

**Constraints:**

* **Land Constraint:** `x + y <= 40` (Total acres available)
* **Fertilizer Cost Constraint:** `50x + 60y <= 4350` (Total budget for fertilizer)
* **Time Constraint:** `60x + 90y <= 6000` (Total time available to lay fertilizer)
* **Non-negativity Constraints:** `x >= 0`, `y >= 0` (Cannot plant negative acres)


```python
import gurobipy as gp
from gurobipy import GRB

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

# Create decision variables
x = m.addVar(lb=0, name="corn_acres")
y = m.addVar(lb=0, name="pea_acres")

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

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

# Optimize the model
m.optimize()

# Print the results
if m.status == GRB.OPTIMAL:
    print(f"Optimal Solution Found:")
    print(f"Plant {x.x} acres of corn")
    print(f"Plant {y.x} acres of peas")
    print(f"Maximum Profit: ${m.objVal}")
elif m.status == GRB.INFEASIBLE:
    print("The model is infeasible. No solution exists.")
else:
    print(f"Optimization terminated with status {m.status}")

```
