## Step 1: Define the symbolic variables
Let's define the symbolic variables for the problem:
- $x_1$ represents the acres of wheat
- $x_2$ represents the acres of corn

## Step 2: Translate the objective function into symbolic notation
The profit per acre of wheat is $50, and the profit per acre of corn is $90. The objective function to maximize profit is:
\[ \text{Maximize:} \quad 50x_1 + 90x_2 \]

## 3: Translate the constraints into symbolic notation
The constraints given are:
- Each acre of wheat requires $10 in maintenance, and each acre of corn requires $20 in maintenance, with at most $1400 available.
- Each acre of wheat requires 4 hours of care, and each acre of corn requires 3 hours of care, with 90 hours available.
- The total acres of land available is 90.

These constraints can be written as:
\[ 10x_1 + 20x_2 \leq 1400 \]
\[ 4x_1 + 3x_2 \leq 90 \]
\[ x_1 + x_2 \leq 90 \]
Also, $x_1 \geq 0$ and $x_2 \geq 0$ since acres cannot be negative.

## 4: Provide the symbolic representation of the problem
The symbolic representation is:
```json
{
'sym_variables': [('x1', 'acres of wheat'), ('x2', 'acres of corn')],
'objective_function': '50*x1 + 90*x2',
'constraints': [
    '10*x1 + 20*x2 <= 1400',
    '4*x1 + 3*x2 <= 90',
    'x1 + x2 <= 90',
    'x1 >= 0',
    'x2 >= 0'
]
}
```

## 5: Write the Gurobi code to solve the problem
```python
import gurobi

def solve_optimization_problem():
    # Create a new model
    model = gurobi.Model()

    # Define the variables
    x1 = model.addVar(lb=0, name="acres_of_wheat")
    x2 = model.addVar(lb=0, name="acres_of_corn")

    # Objective function: Maximize 50*x1 + 90*x2
    model.setObjective(50*x1 + 90*x2, gurobi.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(10*x1 + 20*x2 <= 1400, name="maintenance_cost")
    model.addConstr(4*x1 + 3*x2 <= 90, name="care_hours")
    model.addConstr(x1 + x2 <= 90, name="total_acres")

    # Optimize the model
    model.optimize()

    # Print the results
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Acres of wheat: {x1.varValue}")
        print(f"Acres of corn: {x2.varValue}")
        print(f"Max Profit: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```