Here's how we can formulate this problem and the corresponding Gurobi code:

**Decision Variables:**

* `x`: Number of acres of wheat to plant
* `y`: Number of acres of corn to plant

**Objective Function:**

Maximize profit: `50x + 90y`

**Constraints:**

* **Land Constraint:** `x + y <= 90` (Total acres cannot exceed 90)
* **Maintenance Cost Constraint:** `10x + 20y <= 1400` (Total maintenance cost cannot exceed $1400)
* **Care Time Constraint:** `4x + 3y <= 90` (Total care time cannot exceed 90 hours)
* **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("farm_optimization")

# Create variables
x = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="wheat_acres")
y = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="corn_acres")

# Set objective function
m.setObjective(50*x + 90*y, GRB.MAXIMIZE)

# Add constraints
m.addConstr(x + y <= 90, "land_constraint")
m.addConstr(10*x + 20*y <= 1400, "maintenance_constraint")
m.addConstr(4*x + 3*y <= 90, "care_constraint")

# Optimize model
m.optimize()

# Print results
if m.status == GRB.OPTIMAL:
    print(f"Optimal Solution Found:")
    print(f"Plant {x.x} acres of wheat")
    print(f"Plant {y.x} acres of corn")
    print(f"Maximum Profit: ${m.objVal}")
elif m.status == GRB.INFEASIBLE:
    print("The model is infeasible.")
else:
    print(f"Optimization terminated with status {m.status}")

```
